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• v.28; Jan-Dec 2021

Cancer Biology, Epidemiology, and Treatment in the 21st Century: Current Status and Future Challenges From a Biomedical Perspective

Patricia piña-sánchez.

1 Oncology Research Unit, Oncology Hospital, Mexican Institute of Social Security, Mexico

Antonieta Chávez-González

Martha ruiz-tachiquín, eduardo vadillo, alberto monroy-garcía, juan josé montesinos, rocío grajales.

2 Department of Medical Oncology, Oncology Hospital, Mexican Institute of Social Security, Mexico

Marcos Gutiérrez de la Barrera

3 Clinical Research Division, Oncology Hospital, Mexican Institute of Social Security, Mexico

Hector Mayani

Since the second half of the 20th century, our knowledge about the biology of cancer has made extraordinary progress. Today, we understand cancer at the genomic and epigenomic levels, and we have identified the cell that starts neoplastic transformation and characterized the mechanisms for the invasion of other tissues. This knowledge has allowed novel drugs to be designed that act on specific molecular targets, the immune system to be trained and manipulated to increase its efficiency, and ever more effective therapeutic strategies to be developed. Nevertheless, we are still far from winning the war against cancer, and thus biomedical research in oncology must continue to be a global priority. Likewise, there is a need to reduce unequal access to medical services and improve prevention programs, especially in countries with a low human development index.

Introduction

During the last one hundred years, our understanding of the biology of cancer increased in an extraordinary way. 1 - 4 Such a progress has been particularly prompted during the last few decades because of technological and conceptual progress in a variety of fields, including massive next-generation sequencing, inclusion of “omic” sciences, high-resolution microscopy, molecular immunology, flow cytometry, analysis and sequencing of individual cells, new cell culture techniques, and the development of animal models, among others. Nevertheless, there are many questions yet to be answered and many problems to be solved regarding this disease. As a consequence, oncological research must be considered imperative.

Currently, cancer is one of the illnesses that causes more deaths worldwide. 5 According to data reported in 2020 by the World Health Organization (WHO), cancer is the second cause of death throughout the world, with 10 million deaths. 6 Clearly, cancer is still a leading problem worldwide. With this in mind, the objective of this article is to present a multidisciplinary and comprehensive overview of the disease. We will begin by analyzing cancer as a process, focusing on the current state of our knowledge on 4 specific aspects of its biology. Then, we will look at cancer as a global health problem, considering some epidemiological aspects, and discussing treatment, with a special focus on novel therapies. Finally, we present our vision on some of the challenges and perspectives of cancer in the 21 st century.

The Biology of Cancer

Cancer is a disease that begins with genetic and epigenetic alterations occurring in specific cells, some of which can spread and migrate to other tissues. 4 Although the biological processes affected in carcinogenesis and the evolution of neoplasms are many and widely different, we will focus on 4 aspects that are particularly relevant in tumor biology: genomic and epigenomic alterations that lead to cell transformation, the cells where these changes occur, and the processes of invasion and metastasis that, to an important degree, determine tumor aggressiveness.

Cancer Genomics

The genomics of cancer can be defined as the study of the complete sequence of DNA and its expression in tumor cells. Evidently, this study only becomes meaningful when compared to normal cells. The sequencing of the human genome, completed in 2003, was not only groundbreaking with respect to the knowledge of our gene pool, but also changed the way we study cancer. In the post-genomic era, various worldwide endeavors, such as the Human Cancer Genome Project , the Cancer Genome ATLAS (TCGA), the International Cancer Genome Consortium, and the Pan-Cancer Analysis Working Group (PCAWG), have contributed to the characterization of thousands of primary tumors from different neoplasias, generating more than 2.5 petabytes (10 15 ) of genomic, epigenomic, and proteomic information. This has led to the building of databases and analytical tools that are available for the study of cancer from an “omic” perspective, 7 , 8 and it has helped to modify classification and treatment of various neoplasms.

Studies in the past decade, including the work by the PCAWG, have shown that cancer generally begins with a small number of driving mutations (4 or 5 mutations) in particular genes, including oncogenes and tumor-suppressor genes. Mutations in TP53, a tumor-suppressor gene, for example, are found in more than half of all cancer types as an early event, and they are a hallmark of precancerous lesions. 9 - 12 From that point on, the evolution of tumors may take decades, throughout which the mutational spectrum of tumor cells changes significantly. Mutational analysis of more than 19 000 exomes revealed a collection of genomic signatures, some associated with defects in the mechanism of DNA repair. These studies also revealed the importance of alterations in non-coding regions of DNA. Thus, for example, it has been observed that various pathways of cell proliferation and chromatin remodeling are altered by mutations in coding regions, while pathways, such as WNT and NOTCH, can be disrupted by coding and non-coding mutations. To the present date, 19 955 genes that codify for proteins and 25 511 genes for non-coding RNAs have been identified ( https://www.gencodegenes.org/human/stats.html ). Based on this genomic catalogue, the COSMIC (Catalogue Of Somatic Mutations In Cancer) repository, the most robust database to date, has registered 37 288 077 coding mutations, 19 396 fusions, 1 207 190 copy number variants, and 15 642 672 non-coding variants reported up to August 2020 (v92) ( https://cosmic-blog.sanger.ac.uk/cosmic-release-v92/ ).

The genomic approach has accelerated the development of new cancer drugs. Indeed, two of the most relevant initiatives in recent years are ATOM (Accelerating Therapeutics for Opportunities in Medicine), which groups industry, government and academia, with the objective of accelerating the identification of drugs, 13 and the Connectivity Map (CMAP), a collection of transcriptional data obtained from cell lines treated with drugs for the discovery of functional connections between genes, diseases, and drugs. The CMAP 1.0 covered 1300 small molecules and more than 6000 signatures; meanwhile, the CMAP 2.0 with L1000 assay profiled more than 1.3 million samples and approximately 400 000 signatures. 14

The genomic study of tumors has had 2 fundamental contributions. On the one hand, it has allowed the confirmation and expansion of the concept of intratumor heterogeneity 15 , 16 ; and on the other, it has given rise to new classification systems for cancer. Based on the molecular classification developed by expression profiles, together with mutational and epigenomic profiles, a variety of molecular signatures have been identified, leading to the production of various commercial multigene panels. In breast cancer, for example, different panels have been developed, such as Pam50/Prosigna , Blue Print , OncotypeDX , MammaPrint , Prosigna , Endopredict , Breast Cancer Index , Mammostrat, and IHC4 . 17

Currently, the genomic/molecular study of cancer is more closely integrated with clinical practice, from the classification of neoplasms, as in tumors of the nervous system, 18 to its use in prediction, as in breast cancer. 17 Improvement in molecular methods and techniques has allowed the use of smaller amounts of biological material, as well as paraffin-embedded samples for genomic studies, both of which provide a wealth of information. 19 In addition, non-invasive methods, such as liquid biopsies, represent a great opportunity not only for the diagnosis of cancer, but also for follow-up, especially for unresectable tumors. 20

Research for the production of genomic information on cancer is presently dominated by several consortia, which has allowed the generation of a great quantity of data. However, most of these consortia and studies are performed in countries with a high human development index (HDI), and countries with a low HDI are not well represented in these large genomic studies. This is why initiatives such as Human Heredity and Health in Africa (H3Africa) for genomic research in Africa are essential. 21 Generation of new information and technological developments, such as third-generation sequencing, will undoubtedly continue to move forward in a multidisciplinary and complex systems context. However, the existing disparities in access to genomic tools for diagnosis, prognosis, and treatment of cancer will continue to be a pressing challenge at regional and social levels.

Cancer Epigenetics

Epigenetics studies the molecular mechanisms that produce hereditable changes in gene expression, without causing alterations in the DNA sequence. Epigenetic events are of 3 types: methylation of DNA and RNA, histone modification (acetylation, methylation, and phosphorylation), and the expression of non-coding RNA. Epigenetic aberrations can drive carcinogenesis when they alter chromosome conformation and the access to transcriptional machinery and to various regulatory elements (promoters, enhancers, and anchors for interaction with chromatin, for example). These changes may activate oncogenesis and silence tumor-suppressor mechanisms when they modulate coding and non-coding sequences (such as micro-RNAs and long-RNAs). This can then lead to uncontrolled growth, as well as the invasion and metastasis of cancer cells.

While genetic mutations are stable and irreversible, epigenetic alterations are dynamic and reversible; that is, there are several epigenomes, determined by space and time, which cause heterogeneity of the “epigenetic status” of tumors during their development and make them susceptible to environmental stimuli or chemotherapeutic treatment. 22 Epigenomic variability creates differences between cells, and this creates the need to analyze cells at the individual level. In the past, epigenetic analyses measured “average states” of cell populations. These studies revealed general mechanisms, such as the role of epigenetic marks on active or repressed transcriptional states, and established maps of epigenetic composition in a variety of cell types in normal and cancerous tissue. However, these approaches are difficult to use to examine events occurring in heterogeneous cell populations or in uncommon cell types. This has led to the development of new techniques that permit marking of a sequence on the epigenome and improvement in the recovery yield of epigenetic material from individual cells. This has helped to determine changes in DNA, RNA, and histones, chromatin accessibility, and chromosome conformation in a variety of neoplasms. 23 , 24

In cancer, DNA hypomethylation occurs on a global scale, while hypermethylation occurs in specific genomic loci, associated with abnormal nucleosome positioning and chromatin modifications. This information has allowed epigenomic profiles to be established in different types of neoplasms. In turn, these profiles have served as the basis to identify new neoplasm subgroups. For example, in triple negative breast cancer (TNBC), 25 and in hepatocellular carcinoma, 26 DNA methylation profiles have helped to the identification of distinct subgroups with clinical relevance. Epigenetic approaches have also helped to the development of prognostic tests to assess the sensitivity of cancer cells to specific drugs. 27

Epigenetic traits could be used to characterize intratumoral heterogeneity and determine the relevance of such a heterogeneity in clonal evolution and sensitivity to drugs. However, it is clear that heterogeneity is not only determined by genetic and epigenetic diversity resulting from clonal evolution of tumor cells, but also by the various cell populations that form the tumor microenvironment (TME). 28 Consequently, the epigenome of cancer cells is continually remodeled throughout tumorigenesis, during resistance to the activity of drugs, and in metastasis. 29 This makes therapeutic action based on epigenomic profiles difficult, although significant advances in this area have been reported. 30

During carcinogenesis and tumor progression, epigenetic modifications are categorized by their mechanisms of regulation ( Figure 1A ) and the various levels of structural complexity ( Figure 1B ). In addition, the epigenome can be modified by environmental stimuli, stochastic events, and genetic variations that impact the phenotype ( Figure 1C ). 31 , 32 The molecules that take part in these mechanisms/events/variations are therapeutic targets of interest with potential impact on clinical practice. There are studies on a wide variety of epidrugs, either alone or in combination, which improve antitumor efficacy. 33 However, the problems with these drugs must not be underestimated. For a considerable number of epigenetic compounds still being under study, the main challenge is to translate in vitro efficacy of nanomolar (nM) concentrations into well-tolerated and efficient clinical use. 34 The mechanisms of action of epidrugs may not be sufficiently controlled and could lead to diversion of the therapeutic target. 35 It is known that certain epidrugs, such as valproic acid, produce unwanted epigenetic changes 36 ; thus the need for a well-established safety profile before these drugs can be used in clinical therapy. Finally, resistance to certain epidrugs is another relevant problem. 37 , 38

Epigenetics of cancer. (A) Molecular mechanisms. (B) Structural hierarchy of epigenomics. (C) Factors affecting the epigenome. Modified from Refs. 31 and 32 .

As we learn about the epigenome of specific cell populations in cancer patients, a door opens to the evaluation of sensitivity tests and the search for new molecular markers for detection, prognosis, follow-up, and/or response to treatment at various levels of molecular regulation. Likewise, the horizon expands for therapeutic alternatives in oncology with the use of epidrugs, such as pharmacoepigenomic modulators for genes and key pathways, including methylation of promoters and regulation of micro-RNAs involved in chemoresponse and immune response in cancer. 39 There is no doubt that integrated approaches identifying stable pharmagenomic and epigenomic patterns and their relation with expression profiles and genetic functions will be more and more valuable in our fight against cancer.

Cancer Stem Cells

Tumors consist of different populations of neoplastic cells and a variety of elements that form part of the TME, including stromal cells and molecules of the extracellular matrix. 40 Such intratumoral heterogeneity becomes even more complex during clonal variation of transformed cells, as well as influence the elements of the TME have on these cells throughout specific times and places. 41 To explain the origin of cancer cell heterogeneity, 2 models have been put forward. The first proposes that mutations occur at random during development of the tumor in individual neoplastic cells, and this promotes the production of various tumor populations, which acquire specific growth and survival traits that lead them to evolve according to intratumor mechanisms of natural selection. 42 The second model proposes that each tumor begins as a single cell that possess 2 functional properties: it can self-renew and it can produce several types of terminal cells. As these 2 properties are characteristics of somatic stem cells, 43 the cells have been called cancer stem cells (CSCs). 44 According to this model, tumors must have a hierarchical organization, where self-renewing stem cells produce highly proliferating progenitor cells, unable to self-renew but with a high proliferation potential. The latter, in turn, give rise to terminal cells. 45 Current evidence indicates that both models may coexist in tumor progression. In agreement with this idea, new subclones could be produced as a result of a lack of genetic stability and mutational changes, in addition to the heterogeneity derived from the initial CSC and its descendants. Thus, in each tumor, a set of neoplastic cells with different genetic and epigenetic traits may be found, which would provide different phenotypic properties. 46

The CSC concept was originally presented in a model of acute myeloid leukemia. 47 The presence of CSCs was later proved in chronic myeloid leukemia, breast cancer, tumors of the central nervous system, lung cancer, colon cancer, liver cancer, prostate cancer, pancreatic cancer, melanoma, and cancer of the head and neck, amongst others. In all of these cases, detection of CSCs was based on separation of several cell populations according to expression of specific surface markers, such as CD133, CD44, CD24, CD117, and CD15. 48 It is noteworthy that in some solid tumors, and even in some hematopoietic ones, a combination of specific markers that allow the isolation of CSCs has not been found. Interestingly, in such tumors, a high percentage of cells with the capacity to start secondary tumors has been observed; thus, the terms Tumor Initiating Cells (TIC) or Leukemia Initiating Cells (LIC) have been adopted. 46

A relevant aspect of the biology of CSCs is that, just like normal stem cells, they can self-renew. Such self-renewal guarantees the maintenance or expansion of the tumor stem cell population. Another trait CSCs share with normal stem cells is their quiescence, first described in chronic myeloid leukemia. 49 The persistence of quiescent CSCs in solid tumors has been recently described in colorectal cancer, where quiescent clones can become dominant after therapy with oxaliplatin. 50 In non-hierarchical tumors, such as melanoma, the existence of slow-cycling cells that are resistant to antimitogenic agents has also been proved. 51 Such experimental evidence supports the idea that quiescent CSCs or TICs are responsible for both tumor resistance to antineoplastic drugs and clinical relapse after initial therapeutic success.

In addition to quiescence, CSCs use other mechanisms to resist the action of chemotherapeutic drugs. One of these is their increased numbers: upon diagnosis, a high number of CSCs are observed in most analyzed tumors, making treatment unable to destroy all of them. On the other hand, CSCs have a high number of molecular pumps that expulse drugs, as well as high numbers of antiapoptotic molecules. In addition, they have very efficient mechanisms to repair DNA damage. In general, these cells show changes in a variety of signaling pathways involved in proliferation, survival, differentiation, and self-renewal. It is worth highlighting that in recent years, many of these pathways have become potential therapeutic targets in the elimination of CSCs. 52 Another aspect that is highly relevant in understanding the biological behavior of CSCs is that they require a specific site for their development within the tissue where they are found that can provide whatever is needed for their survival and growth. These sites, known as niches, are made of various cells, both tumor and non-tumor, as well as a variety of non-cellular elements (extracellular matrix [ECM], soluble cytokines, ion concentration gradients, etc.), capable of regulating the physiology of CSCs in order to promote their expansion, the invasion of adjacent tissues, and metastasis. 53

It is important to consider that although a large number of surface markers have been identified that allow us to enrich and prospectively follow tumor stem cell populations, to this day there is no combination of markers that allows us to find these populations in all tumors, and it is yet unclear if all tumors present them. In this regard, it is necessary to develop new purification strategies based on the gene expression profiles of these cells, so that tumor heterogeneity is taken into account, as it is evident that a tumor can include multiple clones of CSCs that, in spite of being functional, are genetically different, and that these clones can vary throughout space (occupying different microenvironments and niches) and time (during the progression of a range of tumor stages). Such strategies, in addition to new in vitro and in vivo assays, will allow the development of new and improved CSC elimination strategies. This will certainly have an impact on the development of more efficient therapeutic alternatives.

Invasion and Metastasis

Nearly 90% of the mortality associated with cancer is related to metastasis. 54 This consists of a cascade of events ( Figure 2 ) that begins with the local invasion of a tumor into surrounding tissues, followed by intravasation of tumor cells into the blood stream or lymphatic circulation. Extravasation of neoplastic cells in areas distant from the primary tumor then leads to the formation of one or more micrometastatic lesions which subsequently proliferate to form clinically detectable lesions. 4 The cells that are able to produce metastasis must acquire migratory characteristics, which occur by a process known as epithelial–mesenchymal transition (EMT), that is, the partial loss of epithelial characteristics and the acquirement of mesenchymal traits. 55

Invasion and metastasis cascade. Invasion and metastasis can occur early or late during tumor progression. In either case, invasion to adjacent tissues is driven by stem-like cells (cancer stem cells) that acquire the epithelial–mesenchymal transition (EMT) (1). Once they reach sites adjacent to blood vessels, tumor cells (individually or in clusters) enter the blood (2). Tumor cells in circulation can adhere to endothelium and extravasation takes place (3). Other mechanisms alternative to extravasation can exist, such as angiopelosis, in which clusters of tumor cells are internalized by the endothelium. Furthermore, at certain sites, tumor cells can obstruct microvasculature and initiate a metastatic lesion right there. Sometimes, a tumor cells that has just exit circulation goes into an MET in order to become quiescent (4). Inflammatory signals can activate quiescent metastatic cells that will proliferate and generate a clinically detectable lesion (5).

Although several of the factors involved in this process are currently known, many issues are still unsolved. For instance, it has not yet been possible to monitor in vivo the specific moment when it occurs 54 ; the microenvironmental factors of the primary tumor that promote such a transition are not known with precision; and the exact moment during tumor evolution in which one cell or a cluster of cells begin to migrate to distant areas, is also unknown. The wide range of possibilities offered by intra- and inter-tumoral heterogeneity 56 stands in the way of suggesting a generalized strategy that could resolve this complication.

It was previously believed that metastasis was only produced in late stages of tumor progression; however, recent studies indicate that EMT and metastasis can occur during the early course of the disease. In pancreatic cancer, for example, cells going through EMT are able to colonize and form metastatic lesions in the liver in the first stages of the disease. 52 , 57 Metastatic cell clusters circulating in peripheral blood (PB) are prone to generate a metastatic site, compared to individual tumor cells. 58 , 59 In this regard, novel strategies, such as the use of micro-RNAs, are being assessed in order to diminish induction of EMT. 60 It must be mentioned, however, that the metastatic process seems to be even more complex, with alternative pathways that do not involve EMT. 61 , 62

A crucial stage in the process of metastasis is the intravasation of tumor cells (alone or in clusters) towards the blood stream and/or lymphatic circulation. 63 These mechanisms are also under intensive research because blocking them could allow the control of spreading of the primary tumor. In PB or lymphatic circulation, tumor cells travel to distant parts for the potential formation of a metastatic lesion. During their journey, these cells must stand the pressure of blood flow and escape interaction with natural killer (NK) cells . 64 To avoid them, tumor cells often cover themselves with thrombocytes and also produce factors such as VEGF, angiopoietin-2, angiopoietin-4, and CCL2 that are involved in the induction of vascular permeability. 54 , 65 Neutrophils also contribute to lung metastasis in the bloodstream by secreting IL-1β and metalloproteases to facilitate extravasation of tumor cells. 64

The next step in the process of metastasis is extravasation, for which tumor cells, alone or in clusters, can use various mechanisms, including a recently described process known as angiopellosis that involves restructuring the endothelial barrier to internalize one or several cells into a tissue. 66 The study of leukocyte extravasation has contributed to a more detailed knowledge of this process, in such a way that some of the proposed strategies to avoid extravasation include the use of integrin inhibitors, molecules that are vital for rolling, adhesion, and extravasation of tumor cells. 67 , 68 Another strategy that has therapeutic potential is the use of antibodies that strengthen vascular integrity to obstruct transendothelial migration of tumor cells and aid in their destruction in PB. 69

Following extravasation, tumor cells can return to an epithelial phenotype, a process known as mesenchymal–epithelial transition and may remain inactive for several years. They do this by competing for specialized niches, like those in the bone marrow, brain, and intestinal mucosa, which provide signals through the Notch and Wnt pathways. 70 Through the action of the Wnt pathway, tumor cells enter a slow state of the cell cycle and induce the expression of molecules that inhibit the cytotoxic function of NK cells. 71 The extravasated tumor cell that is in a quiescent state must comply with 2 traits typical of stem cells: they must have the capacity to self-renew and to generate all of the cells that form the secondary tumor.

There are still several questions regarding the metastatic process. One of the persisting debates at present is if EMT is essential for metastasis or if it plays a more important role in chemoresistance. 61 , 62 It is equally important to know if there is a pattern in each tumor for the production of cells with the capacity to carry out EMT. In order to control metastasis, it is fundamental to know what triggers acquisition of the migratory phenotype and the intrinsic factors determining this transition. Furthermore, it is essential to know if mutations associated with the primary tumor or the variety of epigenetic changes are involved in this process. 55 It is clear that metastatic cells have affinity for certain tissues, depending on the nature of the primary tumor (seed and soil hypothesis). This may be caused by factors such as the location and the direction of the bloodstream or lymphatic fluid, but also by conditioning of premetastatic niches at a distance (due to the large number of soluble factors secreted by the tumor and the recruitment of cells of the immune system to those sites). 72 We have yet to identify and characterize all of the elements that participate in this process. Deciphering them will be of upmost importance from a therapeutic point of view.

Epidemiology of Cancer

Cancer is the second cause of death worldwide; today one of every 6 deaths is due to a type of cancer. According to the International Agency for Research on Cancer (IARC), in 2020 there were approximately 19.3 million new cases of cancer, and 10 million deaths by this disease, 6 while 23.8 million cases and 13.0 million deaths are projected to occur by 2030. 73 In this regard, it is clear the increasing role that environmental factors—including environmental pollutants and processed food—play as cancer inducers and promoters. 74 The types of cancer that produce the greatest numbers of cases and deaths worldwide are indicated in Table 1 . 6

Total Numbers of Cancer Cases and Deaths Worldwide in 2020 by Cancer Type (According to the Global Cancer Observatory, IARC).

Data presented on this table were obtained from Ref. 6.

As shown in Figure 3 , lung, breast, prostate, and colorectal cancer are the most common throughout the world, and they are mostly concentrated in countries of high to very high human development index (HDI). Although breast, prostate, and colorectal cancer have a high incidence, the number of deaths they cause is proportionally low, mostly reflecting the great progress made in their control. However, these data also reveal the types of cancer that require further effort in prevention, precise early detection avoiding overdiagnosis, and efficient treatment. This is the case of liver, lung, esophageal, and pancreatic cancer, where the difference between the number of cases and deaths is smaller ( Figure 3B ). Social and economic transition in several countries has had an impact on reducing the incidence of neoplasms associated with infection and simultaneously produced an increase in the types related to reproductive, dietary, and hormonal factors. 75

Incidence and mortality for some types of cancer in the world. (A) Estimated number of cases and deaths in 2020 for the most frequent cancer types worldwide. (B) Incidence and mortality rates, normalized according to age, for the most frequent cancer types in countries with very high/& high (VH&H; blue) and/low and middle (L&M; red) Human Development Index (HDI). Data include both genders and all ages. Data according to https://gco.iarc.fr/today , as of June 10, 2021.

In the past 3 decades, cancer mortality rates have fallen in high HDI countries, with the exception of pancreatic cancer, and lung cancer in women. Nevertheless, changes in the incidence of cancer do not show the same consistency, possibly due to variables such as the possibility of early detection, exposure to risk factors, or genetic predisposition. 76 , 77 Countries such as Australia, Canada, Denmark, Ireland, New Zealand, Norway, and the United Kingdom have reported a reduction in incidence and mortality in cancer of the stomach, colon, lung, and ovary, as well as an increase in survival. 78 Changes in modifiable risk factors, such as the use of tobacco, have played an important role in prevention. In this respect, it has been estimated that decline in tobacco use can explain between 35% and 45% of the reduction in cancer mortality rates, 79 while the fall in incidence and mortality due to stomach cancer can be attributed partly to the control of Helicobacter pylori infection. 80 Another key factor in the fall of mortality rates in developed countries has been an increase in early detection as a result of screening programs, as in breast and prostate cancer, which have had their mortality rates decreased dramatically in spite of an increase in their incidence. 76

Another important improvement observed in recent decades is the increase in survival rates, particularly in high HDI countries. In the USA, for example, survival rates for patients with prostate cancer at 5 years after initial diagnosis was 28% during 1947–1951; 69% during 1975–1977, and 100% during 2003–2009. Something similar occurred with breast cancer, with a 5-year survival rate of 54% in 1947–1951, 75% in 1975–1977, and 90% in 2003–2009. 81 In the CONCORD 3 version, age-standardize 5-year survival for patients with breast cancer in the USA during 2010–2014 was 90%, and 97% for prostate cancer patients. 82 Importantly, even among high HDI countries, significant differences have been identified in survival rates, being stage of disease at diagnosis, time for access to effective treatment, and comorbidities, the main factors influencing survival in these nations. 78 Unfortunately, survival rates in low HDI countries are significantly lower due to several factors, including lack of information, deficient screening and early detection programs, limited access to treatment, and suboptimal cancer registration. 82 It should be noted that in countries with low to middle HDI, neoplasms with the greatest incidence are those affecting women (breast and cervical cancer), which reflects not only a problem with access to health services, but also a serious inequality issue that involves social, cultural, and even religious obstacles. 83

Up to 42% of incident cases and 47% of deaths by cancer in the USA are due to potentially modifiable risk factors such as use of tobacco, physical activity, diet, and infection. 84 It has been calculated that 2.4 million deaths by cancer, mostly of the lung, can be attributed to tobacco. 73 In 2020, the incidence rate of lung cancer in Western Africa was 2.2, whereas in Polynesia and Eastern Asia was 37.3 and 34.4, respectively. 6 In contrast, the global burden of cancer associated with infection was 15.4%, but in Sub-Saharan Africa it was 30%. 85 Likewise, the incidence of cervical cancer in Eastern Africa was 40.1, in contrast with the USA and Canada that have a rate of 6.2. This makes it clear that one of the challenges we face is the reduction of the risk factors that are potentially modifiable and associated with specific types of cancer.

Improvement of survival rates and its disparities worldwide are also important challenges. Five-year survival for breast cancer—diagnosed during 2010-2014— in the USA, for example, was 90%, whereas in countries like South Africa it was 40%. 82 Childhood leukemia in the USA and several European countries shows a 5-year survival of 90%, while in Latin-American countries it is 50–76%. 86 Interestingly, there are neoplasms, such as pancreatic cancer, for which there has been no significant increase in survival, which remains low (5–15%) both in developed and developing countries. 82

Although data reported on global incidence and mortality gives a general overview on the epidemiology of cancer, it is important to note that there are great differences in coverage of cancer registries worldwide. To date, only 1 out of every 3 countries reports high quality data on the incidence of cancer. 87 For the past 50 years, the IARC has supported population-based cancer registries; however, more than one-third of the countries belonging to the WHO, mainly countries of low and middle income (LMIC), have no data on more than half of the 18 indicators of sustainable development goals. 88 High quality cancer registries only cover 4% of the population in Africa, 8% in Asia, and 7% in Latin America, contrasting with 83% in the USA and Canada, and 33% in Europe. 89 In response to this situation, the Global Initiative for Cancer Registry Development was created in 2012 to generate improved infrastructure to permit greater coverage and better quality registries, especially in countries with low and middle HDI. 88 It is expected that initiatives of this sort in the coming years will allow more and better information to guide strategies for the control of cancer worldwide, especially in developing regions. This will enable survival to be measured over longer periods of time (10, 15, or 20 years), as an effective measure in the control of cancer. The WHO has established as a target for 2025 to reduce deaths by cancer and other non-transmissible diseases by 25% in the population between the ages of 30–69; such an effort requires not only effective prevention measures to reduce incidence, but also more efficient health systems to diminish mortality and increase survival. At the moment, it is an even greater challenge because of the effects of the COVID-19 pandemic which has negatively impacted cancer prevention and health services. 90

Oncologic Treatments

A general perspective.

At the beginning of the 20th century, cancer treatment, specifically treatment of solid tumors, was based fundamentally on surgical resection of tumors, which together with other methods for local control, such as cauterization, had been used since ancient times. 91 At that time, there was an ongoing burst of clinical observations along with interventions sustained on fundamental knowledge about physics, chemistry, and biology. In the final years of the 19 th century and the first half of the 20th, these technological developments gave rise to radiotherapy, hormone therapy, and chemotherapy. 92 - 94 Simultaneously, immunotherapy was also developed, although usually on a smaller scale, in light of the overwhelming progress of chemotherapy and radiotherapy. 95

Thus began the development and expansion of disciplines based on these approaches (surgery, radiotherapy, chemotherapy, hormone therapy, and immunotherapy), with their application evolving ever more rapidly up to their current uses. Today, there is a wide range of therapeutic tools for the care of cancer patients. These include elements that emerged empirically, arising from observations of their effects in various medical fields, as well as drugs that were designed to block processes and pathways that form part of the physiopathology of one or more neoplasms according to knowledge of specific molecular alterations. A classic example of the first sort of tool is mustard gas, originally used as a weapon in war, 96 but when applied for medical purposes, marked the beginning of the use of chemicals in the treatment of malignant neoplasms, that is, chemotherapy. 94 A clear example of the second case is imatinib, designed specifically to selectively inhibit a molecular alteration in chronic myeloid leukemia: the Bcr-Abl oncoprotein. 97

It is on this foundation that today the 5 areas mentioned previously coexist and complement one another. The general framework that motivates this amalgam and guides its development is precision medicine, founded on the interaction of basic and clinical science. In the forecasts for development in each of these fields, surgery is expected to continue to be the fundamental approach for primary tumors in the foreseeable future, as well as when neoplastic disease in the patient is limited, or can be limited by applying systemic or regional elements, before and/or after surgical resection, and it can be reasonably anticipated for the patient to have a significant period free from disease or even to be cured. With regards to technology, intensive exploration of robotic surgery is contemplated. 98

The technological possibilities for radiotherapy have progressed in such a way that it is now possible to radiate neoplastic tissue with an extraordinary level of precision, and therefore avoid damage to healthy tissue. 99 This allows administration of large doses of ionizing radiation in one or a few fractions, what is known as “radiosurgery.” The greatest challenges to the efficacy of this approach are related to radio-resistance in certain neoplasms. Most efforts regarding research in this field are concentrated on understanding the underlying biological mechanisms of the phenomenon and their potential control through radiosensitizers. 100

“Traditional” chemotherapy, based on the use of compounds obtained from plants and other natural products, acting in a non-specific manner on both neoplastic and healthy tissues with a high proliferation rate, continues to prevail. 101 The family of chemotherapeutic drugs currently includes alkylating agents, antimetabolites, anti-topoisomerase agents, and anti-microtubules. Within the pharmacologic perspective, the objective is to attain a high concentration or activity of such molecules in specific tissues while avoiding their accumulation in others, in order to achieve an increase in effectiveness and a reduction in toxicity. This has been possible with the use of viral vectors, for example, that are able to limit their replication in neoplastic tissues, and activate prodrugs of normally nonspecific agents, like cyclophosphamide, exclusively in those specific areas. 102 More broadly, chemotherapy also includes a subgroup of substances, known as molecular targeted therapy, that affect processes in a more direct and specific manner, which will be mentioned later.

There is no doubt that immunotherapy—to be explored next—is one of the therapeutic fields where development has been greatest in recent decades and one that has produced enormous expectation in cancer treatment. 103 Likewise, cell therapy, based on the use of immune cells or stem cells, has come to complement the oncologic therapeutic arsenal. 43 Each and every one of the therapeutic fields that have arisen in oncology to this day continue to prevail and evolve. Interestingly, the foreseeable future for the development of cancer treatment contemplates these approaches in a joint and complementary manner, within the general framework of precision medicine, 104 and sustained by knowledge of the biological mechanisms involved in the appearance and progression of neoplasms. 105 , 106

Immunotherapy

Stimulating the immune system to treat cancer patients has been a historical objective in the field of oncology. Since the early work of William Coley 107 to the achievements reached at the end of the 20 th century, scientific findings and technological developments paved the way to searching for new immunotherapeutic strategies. Recombinant DNA technology allowed the synthesis of cytokines, such as interferon-alpha (IFN-α) and interleukin 2 (IL-2), which were authorized by the US Food and Drug Administration (FDA) for the treatment of hairy cell leukemia in 1986, 108 as well as kidney cancer and metastatic melanoma in 1992 and 1998, respectively. 109

The first therapeutic vaccine against cancer, based on the use of autologous dendritic cells (DCs), was approved by the FDA against prostate cancer in 2010. However, progress in the field of immunotherapy against cancer was stalled in the first decade of the present century, mostly due to failure of several vaccines in clinical trials. In many cases, application of these vaccines was detained by the complexity and cost involved in their production. Nevertheless, with the coming of the concept of immune checkpoint control, and the demonstration of the relevance of molecules such as cytotoxic T-lymphocyte antigen 4 (CTLA-4), and programmed cell death molecule-1 (PD-1), immunotherapy against cancer recovered its global relevance. In 2011, the monoclonal antibody (mAb) ipilimumab, specific to the CTLA-4 molecule, was the first checkpoint inhibitor (CPI) approved for the treatment of advanced melanoma. 110 Later, inhibitory mAbs for PD-1, or for the PD-1 ligand (PD-L1), 111 as well as the production of T cells with chimeric receptors for antigen recognition (CAR-T), 112 which have been approved to treat various types of cancer, including melanoma, non-small cell lung cancer (NSCLC), head and neck cancer, bladder cancer, renal cell carcinoma (RCC), and hepatocellular carcinoma, among others, have changed the paradigm of cancer treatment.

In spite of the current use of anti-CTLA-4 and anti-PD-L1 mAbs, only a subgroup of patients has responded favorably to these CPIs, and the number of patients achieving clinical benefit is still small. It has been estimated that more than 70% of patients with solid tumors do not respond to CPI immunotherapy because either they show primary resistance, or after responding favorably, develop resistance to treatment. 113 In this regard, it is important to mention that in recent years very important steps have been taken to identify the intrinsic and extrinsic mechanisms that mediate resistance to CPI immunotherapy. 114 Intrinsic mechanisms include changes in the antitumor immune response pathways, such as faulty processing and presentation of antigens by APCs, activation of T cells for tumor cell destruction, and changes in tumor cells that lead to an immunosuppressive TME. Extrinsic factors include the presence of immunosuppressive cells in the local TME, such as regulatory T cells, myeloid-derived suppressor cells (MDSC), mesenchymal stem/stromal cells (MSCs), and type 2 macrophages (M2), in addition to immunosuppressive cytokines.

On the other hand, classification of solid tumors as “hot,” “cold,” or “excluded,” depending on T cell infiltrates and the contact of such infiltrates with tumor cells, as well as those that present high tumor mutation burden (TMB), have redirected immunotherapy towards 3 main strategies 115 ( Table 2 ): (1) Making T-cell antitumor response more effective, using checkpoint inhibitors complementary to anti-CTLA-4 and anti-PD-L1, such as LAG3, Tim-3, and TIGT, as well as using CAR-T cells against tumor antigens. (2) Activating tumor-associated myeloid cells including monocytes, granulocytes, macrophages, and DC lineages, found at several frequencies within human solid tumors. (3) Regulating the biochemical pathways in TME that produce high concentrations of immunosuppressive molecules, such as kynurenine, a product of tryptophan metabolism, through the activity of indoleamine 2,3 dioxygenase; or adenosine, a product of ATP hydrolysis by the activity of the enzyme 5’nucleotidase (CD73). 116

Current Strategies to Stimulate the Immune Response for Antitumor Immunotherapy.

Abbreviations: TME, tumor microenvironment; IL, interleukin; TNF, Tumor Necrosis Factor; TNFR, TNF-receptor; CD137, receptor–co-stimulator of the TNFR family; OX40, member number 4 of the TNFR superfamily; CD27/CD70, member of the TNFR superfamily; CD40/CD40L, antigen-presenting cells (APC) co-stimulator and its ligand; GM-CSF, granulocyte-macrophage colony-stimulating factor; IFN, interferon; STING, IFN genes-stimulator; RIG-I, retinoic acid inducible gene-I; MDA5, melanoma differentiation-associated protein 5; CDN, cyclic dinucleotide; ATP, adenosine triphosphate; HMGB1, high mobility group B1 protein; TLR, Toll-like receptor; HVEM, Herpes virus entry mediator; GITR, glucocorticoid-induced TNFR family-related gene; CTLA4, cytotoxic T lymphocyte antigen 4; PD-L1, programmed death ligand-1; TIGIT, T-cell immunoreceptor with immunoglobulin and tyrosine-based inhibition motives; CSF1/CSF1R, colony-stimulating factor-1 and its receptor; CCR2, Type 2 chemokine receptor; PI3Kγ, Phosphoinositide 3-Kinase γ; CXCL/CCL, chemokine ligands; LFA1, lymphocyte function-associated antigen 1; ICAM1, intercellular adhesion molecule 1; VEGF, vascular endothelial growth factor; IDO, indolamine 2,3-dioxigenase; TGF, transforming growth factor; LAG-3, lymphocyte-activation gene 3 protein; TIM-3, T-cell immunoglobulin and mucin-domain containing-3; CD73, 5´nucleotidase; ARs, adenosine receptors; Selectins, cell adhesion molecules; CAR-T, chimeric antigen receptor T cell; TCR-T, T-cell receptor engineered T cell.

Apart from the problems associated with its efficacy (only a small group of patients respond to it), immunotherapy faces several challenges related to its safety. In other words, immunotherapy can induce adverse events in patients, such as autoimmunity, where healthy tissues are attacked, or cytokine release syndrome and vascular leak syndrome, as observed with the use of IL-2, both of which lead to serious hypotension, fever, renal failure, and other adverse events that are potentially lethal. The main challenges to be faced by immunotherapy in the future will require the combined efforts of basic and clinical scientists, with the objective of accelerating the understanding of the complex interactions between cancer and the immune system, and improve treatment options for patients. Better comprehension of immune phenotypes in tumors, beyond the state of PD-L1 and TME, will be relevant to increase immunotherapy efficacy. In this context, the identification of precise tumor antigenicity biomarkers by means of new technologies, such as complete genome sequencing, single cell sequencing, and epigenetic analysis to identify sites or subclones typical in drug resistance, as well as activation, traffic and infiltration of effector cells of the immune response, and regulation of TME mechanisms, may help define patient populations that are good candidates for specific therapies and therapeutic combinations. 117 , 118 Likewise, the use of agents that can induce specific activation and modulation of the response of T cells in tumor tissue, will help improve efficacy and safety profiles that can lead to better clinical results.

Molecular Targeted Therapy

For over 30 years, and based on the progress in our knowledge of tumor biology and its mechanisms, there has been a search for therapeutic alternatives that would allow spread and growth of tumors to be slowed down by blocking specific molecules. This approach is known as molecular targeted therapy. 119 Among the elements generally used as molecular targets there are transcription factors, cytokines, membrane receptors, molecules involved in a variety of signaling pathways, apoptosis modulators, promoters of angiogenesis, and cell cycle regulators. 120

Imatinib, a tyrosine kinase inhibitor for the treatment of chronic myeloid leukemia, became the first targeted therapy in the final years of the 1990s. 97 From then on, new drugs have been developed by design, and today more than 60 targeted therapies have been approved by the FDA for the treatment of a variety of cancers ( Table 3 ). 121 This has had a significant impact on progression-free survival and global survival in neoplasms such as non-small cell lung cancer, breast cancer, renal cancer, and melanoma.

FDA Approved Molecular Targeted Therapies for the Treatment of Solid Tumors.

Abbreviations: mAb, monoclonal antibody; ALK, anaplastic lymphoma kinase; CDK, cyclin-dependent kinase; CTLA-4, cytotoxic lymphocyte antigen-4; EGFR, epidermal growth factor receptor; FGFR, fibroblast growth factor receptor; GIST, gastrointestinal stroma tumor; mTOR, target of rapamycine in mammal cells; NSCLC, non-small cell lung carcinoma; PARP, poli (ADP-ribose) polimerase; PD-1, programmed death protein-1; PDGFR, platelet-derived growth factor receptor; PD-L1, programmed death ligand-1; ER, estrogen receptor; PR, progesterone receptor; TKR, tyrosine kinase receptors; SERM, selective estrogen receptor modulator; TKI, tyrosine kinase inhibitor; VEGFR, vascular endothelial growth factor receptor. Modified from Ref. [ 127 ].

Most drugs classified as targeted therapies form part of 2 large groups: small molecules and mAbs. The former are defined as compounds of low molecular weight (<900 Daltons) that act upon entering the cell. 120 Targets of these compounds are cell cycle regulatory proteins, proapoptotic proteins, or DNA repair proteins. These drugs are indicated based on histological diagnosis, as well as molecular tests. In this group there are multi-kinase inhibitors (RTKs) and tyrosine kinase inhibitors (TKIs), like sunitinib, sorafenib, and imatinib; cyclin-dependent kinase (CDK) inhibitors, such as palbociclib, ribociclib and abemaciclib; poli (ADP-ribose) polimerase inhibitors (PARPs), like olaparib and talazoparib; and selective small-molecule inhibitors, like ALK and ROS1. 122

As for mAbs, they are protein molecules that act on membrane receptors or extracellular proteins by interrupting the interaction between ligands and receptors, in such a way that they reduce cell replication and induce cytostasis. Among the most widely used mAbs in oncology we have: trastuzumab, a drug directed against the receptor for human epidermal growth factor-2 (HER2), which is overexpressed in a subgroup of patients with breast and gastric cancer; and bevacizumab, that blocks vascular endothelial growth factor and is used in patients with colorectal cancer, cervical cancer, and ovarian cancer. Other mAbs approved by the FDA include pembolizumab, atezolizumab, nivolumab, avelumab, ipilimumab, durvalumab, and cemiplimab. These drugs require expression of response biomarkers, such as PD-1 and PD-L1, and must also have several resistance biomarkers, such as the expression of EGFR, the loss of PTEN, and alterations in beta-catenin. 123

Because cancer is such a diverse disease, it is fundamental to have precise diagnostic methods that allow us to identify the most adequate therapy. Currently, basic immunohistochemistry is complemented with neoplastic molecular profiles to determine a more accurate diagnosis, and it is probable that in the near future cancer treatments will be based exclusively on molecular profiles. In this regard, it is worth mentioning that the use of targeted therapy depends on the existence of specific biomarkers that indicate if the patient will be susceptible to the effects of the drug or not. Thus, the importance of underlining that not all patients are susceptible to receive targeted therapy. In certain neoplasms, therapeutic targets are expressed in less than 5% of the diagnosed population, hindering a more extended use of certain drugs.

The identification of biomarkers and the use of new generation sequencing on tumor cells has shown predictive and prognostic relevance. Likewise, mutation analysis has allowed monitoring of tumor clone evolution, providing information on changes in canonic gene sequences, such as TP53, GATA3, PIK3CA, AKT1, and ERBB2; infrequent somatic mutations developed after primary treatments, like SWI-SNF and JAK2-STAT3; or acquired drug resistance mutations such as ESR1. 124 The study of mutations is vital; in fact, many of them already have specific therapeutic indications, which have helped select adequate treatments. 125

There is no doubt that molecular targeted therapy is one of the main pillars of precision medicine. However, it faces significant problems that often hinder obtaining better results. Among these, there is intratumor heterogeneity and differences between the primary tumor and metastatic sites, as well as intrinsic and acquired resistance to these therapies, the mechanisms of which include the presence of heterogeneous subclones, DNA hypermethylation, histone acetylation, and interruption of mRNA degradation and translation processes. 126 Nonetheless, beyond the obstacles facing molecular targeted therapy from a biological and methodological point of view, in the real world, access to genomic testing and specific drugs continues to be an enormous limitation, in such a way that strategies must be designed in the future for precision medicine to be possible on a global scale.

Cell Therapy

Another improvement in cancer treatment is the use of cell therapy, that is, the use of specific cells as therapeutic agents. This clinical procedure has 2 modalities: the first consists of replacing and regenerating functional cells in a specific tissue by means of stem/progenitor cells of a certain kind, 43 while the second uses immune cells as effectors to eliminate malignant cells. 127

Regarding the first type, we must emphasize the development of cell therapy based on hematopoietic stem and progenitor cells. 128 For over 50 years, hematopoietic cell transplants have been used to treat a variety of hematologic neoplasms (different forms of leukemia and lymphoma). Today, it is one of the most successful examples of cell therapy, including innovative modalities, such as haploidentical transplants, 129 as well as application of stem cells expanded ex vivo . 130 There are also therapies that have used immature cells that form part of the TME, such as MSCs. The replication potential and cytokine secretion capacity of these cells make them an excellent option for this type of treatment. 131 Neural stem cells can also be manipulated to produce and secrete apoptotic factors, and when these cells are incorporated into primary neural tumors, they cause a certain degree of regression. They can even be transfected with genes that encode for oncolytic enzymes capable of inducing regression of glioblastomas. 132

With respect to cell therapy using immune cells, several research groups have manipulated cells associated with tumors to make them effector cells and thus improve the efficacy and specificity of the antitumor treatment. PB leckocytes cultured in the presence of IL-2 to obtain activated lymphocytes, in combination with IL-2 administration, have been used in antitumor clinical protocols. Similarly, infiltrating lymphocytes from tumors with antitumor activity have been used and can be expanded ex vivo with IL-2. These lymphocyte populations have been used in immunomodulatory therapies in melanoma, and pancreatic and kidney tumors, producing a favorable response in treated patients. 133 NK cells and macrophages have also been used in immunotherapy, although with limited results. 134 , 135

One of the cell therapies with better projection today is the use of CAR-T cells. This strategy combines 2 forms of advanced therapy: cell therapy and gene therapy. It involves the extraction of T cells from the cancer patient, which are genetically modified in vitro to express cell surface receptors that will recognize antigens on the surface of tumor cells. The modified T cells are then reintroduced in the patient to aid in an exacerbated immune response that leads to eradication of the tumor cells ( Figure 4 ). Therapy with CAR-T cells has been used successfully in the treatment of some types of leukemia, lymphoma, and myeloma, producing complete responses in patients. 136

CAR-T cell therapy. (A) T lymphocytes obtained from cancer patients are genetically manipulated to produce CAR-T cells that recognize tumor cells in a very specific manner. (B) Interaction between CAR molecule and tumor antigen. CAR molecule is a receptor that results from the fusion between single-chain variable fragments (scFv) from a monoclonal antibody and one or more intracellular signaling domains from the T-cell receptor. CD3ζ, CD28 and 4-1BB correspond to signaling domains on the CAR molecule.

Undoubtedly, CAR-T cell therapy has been truly efficient in the treatment of various types of neoplasms. However, this therapeutic strategy can also have serious side effects, such as release of cytokines into the bloodstream, which can cause different symptoms, from high fever to multiorgan failure, and even neurotoxicity, leading to cerebral edema in many cases. 137 Adequate control of these side effects is an important medical challenge. Several research groups are trying to improve CAR-T cell therapy through various approaches, including production of CAR-T cells directed against a wider variety of tumor cell-specific antigens that are able to attack different types of tumors, and the identification of more efficient types of T lymphocytes. Furthermore, producing CAR-T cells from a single donor that may be used in the treatment of several patients would reduce the cost of this sort of personalized cell therapy. 136

Achieving wider use of cell therapy in oncologic diseases is an important challenge that requires solving various issues. 138 One is intratumor cell heterogeneity, including malignant subclones and the various components of the TME, which results in a wide profile of membrane protein expression that complicates finding an ideal tumor antigen that allows specific identification (and elimination) of malignant cells. Likewise, structural organization of the TME challenges the use of cell therapy, as administration of cell vehicles capable of recognizing malignant cells might not be able to infiltrate the tumor. This results from low expression of chemokines in tumors and the presence of a dense fibrotic matrix that compacts the inner tumor mass and avoids antitumor cells from infiltrating and finding malignant target cells.

Further Challenges in the 21st Century

Beyond the challenges regarding oncologic biomedical research, the 21 st century is facing important issues that must be solved as soon as possible if we truly wish to gain significant ground in our fight against cancer. Three of the most important have to do with prevention, early diagnosis, and access to oncologic medication and treatment.

Prevention and Early Diagnosis

Prevention is the most cost-effective strategy in the long term, both in low and high HDI nations. Data from countries like the USA indicate that between 40-50% of all types of cancer are preventable through potentially modifiable factors (primary prevention), such as use of tobacco and alcohol, diet, physical activity, exposure to ionizing radiation, as well as prevention of infection through access to vaccination, and by reducing exposure to environmental pollutants, such as pesticides, diesel exhaust particles, solvents, etc. 74 , 84 Screening, on the other hand, has shown great effectiveness as secondary prevention. Once population-based screening programs are implemented, there is generally an initial increase in incidence; however, in the long term, a significant reduction occurs not only in incidence rates, but also in mortality rates due to detection of early lesions and timely and adequate treatment.

A good example is colon cancer. There are several options for colon cancer screening, such as detection of fecal occult blood, fecal immunohistochemistry, flexible sigmoidoscopy, and colonoscopy, 139 , 140 which identify precursor lesions (polyp adenomas) and allow their removal. Such screening has allowed us to observe 3 patterns of incidence and mortality for colon cancer between the years 2000 and 2010: on one hand, an increase in incidence and mortality in countries with low to middle HDI, mainly countries in Asia, South America, and Eastern Europe; on the other hand, an increase in incidence and a fall in mortality in countries with very high HDI, such as Canada, the United Kingdom, Denmark, and Singapore; and finally a fall in incidence and mortality in countries like the USA, Japan, and France. The situation in South America and Asia seems to reflect limitations in medical infrastructure and a lack of access to early detection, 141 while the patterns observed in developed countries reveal the success, even if it may be partial, of that which can be achieved by well-structured prevention programs.

Another example of success, but also of strong contrast, is cervical cancer. The discovery of the human papilloma virus (HPV) as the causal agent of cervical cancer brought about the development of vaccines and tests to detect oncogenic genotypes, which modified screening recommendations and guidelines, and allowed several developed countries to include the HPV vaccine in their national vaccination programs. Nevertheless, the outlook is quite different in other areas of the world. Eighty percent of the deaths by cervical cancer reported in 2018 occurred in low-income nations. This reveals the urgency of guaranteeing access to primary and secondary prevention (vaccination and screening, respectively) in these countries, or else it will continue to be a serious public health problem in spite of its preventability.

Screening programs for other neoplasms, such as breast, prostate, lung, and thyroid cancer have shown outlooks that differ from those just described, because, among other reasons, these neoplasms are highly diverse both biologically and clinically. Another relevant issue is the overdiagnosis of these neoplasms, that is, the diagnosis of disease that would not cause symptoms or death in the patient. 142 It has been calculated that 25% of breast cancer (determined by mammogram), 50–60% of prostate cancer (determined by PSA), and 13–25% of lung cancer (determined by CT) are overdiagnosed. 142 Thus, it is necessary to improve the sensitivity and specificity of screening tests. In this respect, knowledge provided by the biology of cancer and “omic” sciences offers a great opportunity to improve screening and prevention strategies. All of the above shows that prevention and early diagnosis are the foundations in the fight against cancer, and it is essential to continue to implement broader screening programs and better detection methods.

Global Equity in Oncologic Treatment

Progress in cancer treatment has considerably increased the number of cancer survivors. Nevertheless, this tendency is evident only in countries with a very solid economy. Indeed, during the past 30 years, cancer mortality rates have increased 30% worldwide. 143 Global studies indicate that close to 70% of cancer deaths in the world occur in nations of low to middle income. But even in high-income countries, there are sectors of society that are more vulnerable and have less access to cancer treatments. 144 Cancer continues to be a disease of great social inequality.

In Europe, the differences in access to cancer treatment are highly marked. These treatments are more accessible in Western Europe than in its Eastern counterpart. 145 Furthermore, highly noticeable differences between high-income countries have been detected in the cost of cancer drugs. 146 It is interesting to note that in many of these cases, treatment is too costly and the clinical benefit only marginal. Thus, the importance of these problems being approached by competent national, regional, and global authorities, because if these new drugs and therapeutic programs are not accessible to the majority, progress in biomedical, clinical and epidemiological research will have a limited impact in our fight against cancer. We must not forget that health is a universal right, from which low HDI countries must not be excluded, nor vulnerable populations in nations with high HDI. The participation of a well-informed society will also be fundamental to achieve a global impact, as today we must fight not only against the disease, but also against movements and ideas (such as the anti-vaccine movement and the so-called miracle therapies) that can block the medical battle against cancer.

Final Comments

From the second half of the 20th century to the present day, progress in our knowledge about the origin and development of cancer has been extraordinary. We now understand cancer in detail in genomic, molecular, cellular, and physiological terms, and this knowledge has had a significant impact in the clinic. There is no doubt that a patient who is diagnosed today with a type of cancer has a better prospect than a patient diagnosed 20 or 50 years ago. However, we are still far from winning the war against cancer. The challenges are still numerous. For this reason, oncologic biomedical research must be a worldwide priority. Likewise, one of the fundamental challenges for the coming decades must be to reduce unequal access to health services in areas of low- to middle income, and in populations that are especially vulnerable, as well as continue improving prevention programs, including public health programs to reduce exposure to environmental chemicals and improve diet and physical activity in the general population. 74 , 84 Fostering research and incorporation of new technological resources, particularly in less privileged nations, will play a key role in our global fight against cancer.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Hector Mayani https://orcid.org/0000-0002-2483-3782

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• Published: 16 May 2024

A guide to artificial intelligence for cancer researchers

• Raquel Perez-Lopez   ORCID: orcid.org/0000-0002-9176-0130 1 ,
• Narmin Ghaffari Laleh   ORCID: orcid.org/0000-0003-0889-3352 2 ,
• Faisal Mahmood   ORCID: orcid.org/0000-0001-7587-1562 3 , 4 , 5 , 6 , 7 , 8 &
• Jakob Nikolas Kather   ORCID: orcid.org/0000-0002-3730-5348 2 , 9 , 10  

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Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.

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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology

Guiding principles for the responsible development of artificial intelligence tools for healthcare

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Acknowledgements

R.P.-L. is supported by LaCaixa Foundation, a CRIS Foundation Talent Award (TALENT19-05), the FERO Foundation, the Instituto de Salud Carlos III-Investigacion en Salud (PI18/01395 and PI21/01019), the Prostate Cancer Foundation (18YOUN19) and the Asociación Española Contra el Cancer (AECC) (PRYCO211023SERR). J.N.K. is supported by the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A; and TANGERINE, 01KT2302 through ERA-NET Transcan), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (TransplantKI, 01VSF21048), the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091; and GENIAL, 101096312), the European Research Council (ERC; NADIR, 101114631) and the National Institute for Health and Care Research (NIHR; NIHR203331) Leeds Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

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J.N.K. declares consulting services for Owkin, DoMore Diagnostics, Panakeia, Scailyte, Mindpeak and MultiplexDx; holds shares in StratifAI GmbH; has received a research grant from GSK; and has received honoraria from AstraZeneca, Bayer, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius. R.P.-L. declares research funding by AstraZeneca and Roche, and participates in the steering committee of a clinical trial sponsored by Roche, not related to this work. All other authors declare no competing interests.

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(API). A set of tools and protocols for building software and applications, enabling software to communicate with AI models.

(ANNs). Computational models loosely inspired by the structure and function of the human brain, consisting of interconnected layers of nodes, called neurons, that process input data and learn to recognize patterns and make decisions.

The use of algorithms, machine learning and image analysis techniques to extract information from digital pathology images.

A field of AI that focuses on enabling computers to analyse and interpret visual data, such as images and videos.

(CNNs). A type of deep neural network that is especially effective for analysing visual imagery and used in image analysis.

Deep learning is a subfield of machine learning that uses artificial neural networks with multiple layers, called deep neural networks, to learn and extract highly complex features and patterns from raw input data.

Visual representations captured and stored in a digital format, consisting of a grid of pixels, with each pixel representing a colour intensity value.

The practice of converting glass slides into digital slides that can be viewed, managed and analysed on a computer.

Techniques in AI that provide insights and explanations on how the AI model arrived at its conclusions, thus making the decision-making process of the AI more transparent.

AI systems that can generate new content (text, images or music) that is similar to the content on which it was trained, often creating novel and coherent outputs.

Extremely high-resolution digital images consisting of 1 billion pixels, obtained by scanning tissue slides with a slide scanner.

(GPUs). Specialized hardware used to rapidly process large blocks of data simultaneously, used in computer gaming and AI.

(LLMs). Advanced AI models trained on vast amounts of text data, capable of analysing, generating and manipulating human language, often at the human level 174 .

A type of neural network particularly good at processing sequences of data (such as time series or language), with a capability to remember information for a certain time.

A subset of AI focusing on the development of algorithms and models that enable computers to learn and improve their performance on a specific task without being explicitly instructed how to achieve this.

(NLP). A branch of AI that helps computers to analyse, interpret and respond to human language in a useful way.

Crafting inputs or questions in a way that guides AI models, particularly LLMs, to provide the most effective and accurate responses.

Types of a neural network model that excel at processing sequences of data, such as sentences in text, by focusing on different parts of the sequence to make predictions 175 .

The three-dimensional equivalent of a pixel in images, representing a value on a regular grid in three-dimensional space, commonly used in medical imaging such as MRI and CT scans.

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Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. et al. A guide to artificial intelligence for cancer researchers. Nat Rev Cancer (2024). https://doi.org/10.1038/s41568-024-00694-7

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DOI : https://doi.org/10.1038/s41568-024-00694-7

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Article Contents

Introduction, materials and methods, data availability, supplementary data, acknowledgements, prmt5-mediated arginine methylation of fxr1 is essential for rna binding in cancer cells.

The first two authors should be regarded as Joint First Authors.

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Anitha Vijayakumar, Mrinmoyee Majumder, Shasha Yin, Charles Brobbey, Joseph Karam, Breege Howley, Philip H Howe, Stefano Berto, Lalima K Madan, Wenjian Gan, Viswanathan Palanisamy, PRMT5-mediated arginine methylation of FXR1 is essential for RNA binding in cancer cells, Nucleic Acids Research , 2024;, gkae319, https://doi.org/10.1093/nar/gkae319

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Emerging evidence indicates that arginine methylation promotes the stability of arginine-glycine-rich (RGG) motif-containing RNA-binding proteins (RBPs) and regulates gene expression. Here, we report that post-translational modification of FXR1 enhances the binding with mRNAs and is involved in cancer cell growth and proliferation. Independent point mutations in arginine residues of FXR1’s nuclear export signal (R386 and R388) and RGG (R453, R455 and R459) domains prevent it from binding to RNAs that form G-quadruplex (G4) RNA structures. Disruption of G4-RNA structures by lithium chloride failed to bind with FXR1, indicating its preference for G4-RNA structure containing mRNAs. Furthermore, loss-of-function of PRMT5 inhibited FXR1 methylation both in vivo and in vitro , affecting FXR1 protein stability, inhibiting RNA-binding activity and cancer cell growth and proliferation. Finally, the enhanced crosslinking and immunoprecipitation (eCLIP) analyses reveal that FXR1 binds with the G4-enriched mRNA targets such as AHNAK, MAP1B, AHNAK2, HUWE1, DYNC1H1 and UBR4 and controls its mRNA expression in cancer cells. Our findings suggest that PRMT5-mediated FXR1 methylation is required for RNA/G4-RNA binding, which promotes gene expression in cancer cells. Thus, FXR1’s structural characteristics and affinity for RNAs preferentially G4 regions provide new insights into the molecular mechanism of FXR1 in oral cancer cells.

Dysregulated gene expression is a hallmark of cancer, and post-transcriptional gene regulation (PTR) contributes significantly to activating oncogenes and reducing tumor suppressor expression ( 1 , 2 ). The changes in PTR have gained considerable attention for their regulatory roles in biologically significant cis- and trans-factors, such as 5′- and 3′-untranslated regions (UTRs) of mRNAs and RNA-binding proteins (RBPs), respectively ( 3 ). RBPs regulate critical cellular processes, including transcription, mRNA turnover, and translation ( 4 ). However, aberrant expression of RBPs contributes to neoplasia, including head and neck oral squamous cell carcinomas ( 5 , 6 ). Although significant progress has been achieved in understanding RBP-mediated gene regulation ( 7 , 8 ), and cancer-promoting activity, the molecular basis of dysregulated expression of RBPs has yet to be studied. RBP, Fragile X mental retardation protein-1 (FXR1), is a chromosome 3q amplification gene overexpressed in multiple cancers and exerts oncogenic signaling to promote tumorigenesis ( 9–16 ). Our published findings indicate that FXR1 helps cancer cells bypass cellular senescence by stabilizing the non-coding telomerase RNA component (TERC) and destabilizing CDKN1A (p21) to promote cell growth ( 16 ). Furthermore, our findings also demonstrated that FXR1 targets p21 mRNA destabilization by recruiting miR-301a-3p in both oral and lung cancer cells ( 17 ). Although FXR1, its downstream targets, and p53/p21 pathway-mediated cellular senescence are well studied in oral and lung cancer cells, it remains unclear how elevated FXR1 protein enhances malignant transformation in cancer cells. As most RBPs undergo post-translational modifications (PTM) such as phosphorylation, acetylation, methylation, and sumoylation to regulate gene expression in cancer cells ( 18 ), here, we set out to study the impact of PTM on FXR1 and its regulatory effects on its RNA targets. Based on the observation and unproven hypothesis that FXR1 is targeted by protein methyltransferases ( 19 ), we focused on identifying and characterizing enzymes that methylate FXR1 at the post-translational level and report the functional interactions between FXR1 and methyltransferases.

For the past 30 years, several attempts have been made to understand the biological functions of Fragile-X mental retardation (FXR) proteins in Fragile-X syndrome ( 20 ). Still, a significant knowledge gap exists in appreciating the role of the FXR family of proteins in cancer cell structure, function, protein modifications, and RNA metabolism ( 21 ). The FXR family members FMRP and FXR1 contain the arginine/glycine-rich (RGG) protein domain, but FXR2 lacks the RGG domain. However, all three FXR families of proteins have K-homology domains, which are ubiquitous throughout eukaryotes ( 22 ). FXR1 contains highly conserved arginine residues in its C-terminal nuclear export signal (NES) and the RGG domain. About 0.5–1% of the total arginine residues in the human proteome are methylated and have a slow turnover rate, which will likely confer long-lasting functional properties to the target proteins ( 23 , 24 ). Adding a methyl group(s) to the arginine residues helps the proteins to interact with other proteins and nucleic acids ( 25 ). The protein arginine methyltransferases termed PRMTs (PRMT1, 3, 4 [CARM1], 5, 6 and 8), and other probable methyltransferases (PRMT2, 7, 9) are responsible for protein methylation ( 26 ). Although the RGG domain functions are relatively known, its biological significance is bypassed in the FXR family of proteins that regulate all levels of RNA metabolism ( 27–29 ). It was envisioned that the FXR1 RGG domain could be a target of arginine methyltransferases for methylation ( 30 ). However, the specific arginine methyltransferase responsible for the methylation of FXR1 has never been identified. Methylation of FMRP and FXR1 occurs mainly within their RGG box, which may influence their RNA-binding and protein-protein interactions ( 19 ). Hence, in this research, we investigated the effect of arginine methylation on FXR1’s RNA binding capacity including its specificity towards guanine rich regions in cancer cells.

FXR1 is known to be involved in mRNA transport, translational control, and mRNA binding via adenylate-uridylate-rich (AU-rich) elements (ARE) ( 31 , 32 ), and G-quartet (G4) RNA structures ( 33 , 34 ). Previous studies have shown that FXR1 undergoes distinct PTM ( 35 , 36 ). However, the enzyme responsible for FXR1’s methylation and how methylated FXR1 impacts RNA binding and alters their expression in cancer cells are unclear. For the first time, here we report, FXR1 is arginine methylated and the functional consequence of methylation relating to RNA binding activity in cancer cells. This study shows that PRMT5 interacts with FXR1 and methylates arginine at positions 386, 388, 453, 455 and 459. Interestingly, both R388 and R455 of FXR1 are necessary to bind to RNAs, with a predilection for G4-RNAs. As a result, we argue that FXR1 methylation increases its G4-RNA-binding capacity, which promotes cancer cell growth and proliferation. Furthermore, the FXR1 mRNA targets identified by nhanced crosslinking and immunoprecipitation (eCLIP)-seq had a greater binding affinity for the G4-rich sequences of top genes such AHNAK, AHNAK2, UBR4, MAP1B, DYNC1H1 and HUWE1. Studies have found that these targets have many functions in various malignancies ( 37–41 ). In addition, TCGA database analysis of HNSCC revealed amplification of these RNA targets, implying carcinogenic involvement. However, further study is required to unravel the molecular mechanism by which FXR1 regulates each of its mRNA targets to promote cancer growth. Interestingly, both genetic and small molecule PRMT5 inhibition failed to methylate recombinant as well as the endogenous FXR1, resulting in protein instability and downregulation of FXR1 target mRNA levels in HNSCC cells. Our findings explain one of the molecular mechanisms of FXR1’s reported tumorigenic role in HNSCC and lay the groundwork for future research into how targeting the interface between FXR1 and PRMT5 can affect gene expression and aid in the development of novel therapies.

Cell lines, reagents, plasmids and antibodies

HNSCC cell lines UMSCC11A, -74A and -74B were obtained from the University of Michigan, and SCC4 (#CRL-1624), SCC25 (#CRL-1628) and Cal27 (#CRL-2095) were obtained from ATCC. Lung cancer cell line A549 was also obtained from ATCC. Cell lines UMSCC74B and Cal27, and A549 were routinely grown in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS) with 100 U/ml penicillin-streptomycin (P/S). UMSCC11A and -74A were grown in DMEM containing 10% FBS, 100 U/ml P/S, and 1X non-essential amino acids. SCC4 and SCC25 cell lines were grown in DMEM: F12 (1:1) containing 400 ng/ml hydrocortisone, 10% FBS, and 100 U/ml P/S. A549 was grown in F-12K medium containing 10% FBS and 100 U/ml P/S. shRNA constructs for FXR1 (TRCN0000158932) ( 16 , 17 ) were obtained from Sigma Mission. PRMT5 shRNA was obtained from Santa Cruz biotechnologies (SC41073-SH). Flag-PRMT5 and Flag-MEP50 were generated by cloning the corresponding cDNA into the pRK5-Flag vector ( 37 ). HA-PRMT5 was constructed by cloning the corresponding cDNA into the pRK5-HA vector ( 37 ). Myc-FXR1 was constructed by cloning the corresponding FXR1 (>NM_005087.4) into the pCDNA3.0 with C-terminal Myc-tag ( 35 ). GST-FXR1 was created by cloning (S382-P476) of FXR1 (>NM_005087.4) in the C-terminus of GST gene in pGEX-6P-1 plasmid between EcoR1 and NotI with an intervening stop codon. Single guide RNAs (sgRNA) for PRMT5 were designed at https://www.synthego.com and were cloned into lentiCRISPR v2 vector (Addgene, #52961) ( 42 , 43 ).

From Cell Signaling Technology, FXR1 (#12295, used predominantly for western blot), Myc-tag (9B11) (#2276), E-Cadherin (24E10) (#3195), N-Cadherin (D4R1H) (#13116), Symmetric Di-Methyl Arginine Motif [sdme-RG] MultiMab™ Rabbit mAb mix (#13222), Asymmetric Di-Methyl Arginine Motif [adme-R] MultiMab™ Rabbit mAb mix (#13522), CD3 (#78588S); From EMD Millipore, FXR1 (#05-1529, used for IP and RNA-IP); From Abcam, FXR1 (#ab50841 for IHC and multiplex); BD Pharmingen, p21, (#556431); From Proteintech, GAPDH (10494-1-AP), Histone H3 (17168-1-AP), GST-tag (10000-0-AP), PRMT5 (18436-1-AP), PRMT1 (11279-1-AP), HA-tag (51064-2-AP), and β-Actin (60008-1-Ig). Horseradish peroxidase-conjugated anti-mouse (NA931) and anti-rabbit (NA934) immunoglobulin Gs were procured from GE Healthcare Biosciences (Uppsala, Sweden). Normal mouse (sc-2025) and rabbit (sc-2027) IgGs were obtained from Santa Cruz Biotechnology. Protein A/G plus (sc-2003) beads were purchased from Santa Cruz Biotechnology. GSK3326593 (PRMT5) and GSK3368712 (PRMT1) inhibitors were obtained from GlaxoSmithKline (GSK) with a material transfer agreement (MTA). The protein thermal shift assay dye was procured from applied biosystems.

Senescence staining

SA-β-gal activity is determined using X-gal (5-bromo-4-chloro-3-indolyl β- d -galactoside) staining at pH 6.0 according to the manufacturer's instructions (9860, Cell Signaling Technology). A549 cells were transiently infected with Control shRNA and FXR1 shRNA for 72 h and were treated with TGFβ as described in the results section.

Immunoblot analysis

Cells were lysed using RIPA buffer, supplemented with 1× protease inhibitor cocktail and PMSF, equal amount of proteins were separated using SDS-PAGE. Proteins were transferred to the PVDF membrane, blocked in 5% skimmed milk, and incubated with primary antibodies at 4°C overnight. Membranes were washed three times with 1× Tris-buffered saline-0.1% Tween-20 and incubated with secondary antibody for 1 h at room temperature. Proteins were visualized using substrates Clarity or Clarity Max (Biorad# 1705060 and 1705062), followed by Biorad Image Lab.

Polysome profiling

A549 cells were treated with TGFβ for 48 h, cells were lysed in TMK100 lysis buffer, and the supernatant was layered onto a 10–50% sucrose gradient and centrifuged at 151 000 × g at 4°C for 3 h. Polysome fractions were collected using a fraction collector with continuous absorbance monitoring at 254 nM. RNAs were extracted with Trizol (Invitrogen) and reverse-transcribed to cDNAs using SuperScript III Reverse Transcriptase. PCR was performed using primers listed below: FXR1: 5′- CCCTAATTACACCTCCGGTTATG-3′ and 5′-TCTCCTGCCAATGACCAATC-3′; β-Actin: 5′- GGACCTGACTGACTACCTCAT-3′ and 5′-CGTAGCACAGCTTCTCCTTAAT-3′. Two percent agarose gel was utilized to resolve the PCR products. Band quantification was performed using Quantity One (Bio-Rad Laboratories, Inc.).

RNA extraction and qRT-PCR

Total RNA is prepared from HNSCC cell lines using Trizol (Ambion) or RNeasy mini kit (QIAGEN) by following the manufacturer's protocol. qRT-PCR for all m/RNA targets is performed using an Applied Biosystems StepOne Plus system or quantstudio 6.0 pro with the SYBR green master mix RT-PCR kit (SA Biosciences) as described ( 44 ). Primer sequences are provided in Supplementary Table S1 .

RNA-seq mapping and quantification

Reads were aligned to the human hg38 reference genome using STAR (v2.7.10a) ( 45 ). Genecode annotation for hg38 (version 37) was used as reference alignment annotation and downstream quantification. BAM files were filtered for uniquely mapped reads using custom bash scripts. Quality metrics were calculated using Picard tool ( http://broadinstitute.github.io/picard/ ) and summarized using MultiQC ( 46 ). Gene level expression quantification was calculated using FeatureCounts (v2.0.1) ( 47 ). Counts were calculated based on protein-coding genes from the annotation file.

Differential gene expression analysis and functional enrichment

Low-expressed genes were filtered using a per case-control approach with RPKM ≥0.5 as a filter to keep genes. Differential Expression was performed in R using DESeq2 ( 48 ). We estimated log 2 fold changes, P values, and FDR (Benjamini-Hochberg correction). We used FDR <0.05 and abs (log 2 (fold change)) ≥0.5 thresholds to define differentially expressed genes. Custom R codes were used to visualize the data. The functional annotation was performed using the R package clusterProfiler ( 49 ) using the GO database. GSEA analysis was performed using the R package fgsea. A Benjamini–Hochberg FDR was applied as a correction for multiple comparisons. Significant categories were filtered for FDR <0.05.

Transduction (shRNA or sgRNA)

Specific shRNA and control shRNA plasmids or sgRNA and controls were used for the preparation of individual lentiviral particles. Cells were transduced with the lentiviral particles at an MOI (multiplicity of infection) of 25–50 in a medium supplemented with 8 μg/ml polybrene ( 16 ) and incubated for 72 h. mRNA levels and the protein expression were analyzed by qRT-PCR and immunoblot respectively.

Purification of GST-tagged FXR1 proteins from bacteria

50 ml of log phase culture of E. coli BL21(DE3) cells containing the pGEX-6P-1-FXR1 plasmid was grown at 37°C in Luria Broth (LB) containing 100 μg/ml carbenicillin. The bacteria were induced to express human truncated FXR1 protein by adding isopropyl β- d -1-thiolgalactopyranoside (IPTG) to a final concentration of 25 uM and incubated for 4h. Cells were harvested by centrifugation at 2500 × g for 10 min at 4°C, resuspended in 10 ml lysis buffer (50 mM HEPES pH 7.9, 150 mM KCl, 1 mM MgCl2, 0.1% Triton-X 100, 0.1 mM phenylmethylsulfonylfluoride (PMSF), and Complete Protease Inhibitor Cocktail (Fisher#P178430) and lysed via sonication on ice (Fisher Scientific Sonic Dismembrator Model 100; three 10 s pulses at level 7). Debris was pelleted via centrifugation at 11 000 × g for 20 minutes at 4°C, and supernatants were added to glutathione sepharose beads for 3 h at 4°C. Beads were rocked with lysates for 1 h at 4°C, then washed 5 times with 2 ml of lysis buffer. GST-FXR1 protein was eluted by adding 0.1 ml of lysis buffer containing 50 mM reduced glutathione and a batch elution method. Eluted samples were dialyzed into a lysis buffer containing 10% glycerol and stored at −80°C.

In vitro methylation assays

PRMT5 in vitro methylation assays were performed as previously described ( 50 ). Briefly, 5 μg of recombinant GST-FXR1 proteins (WT and mutants) purified from bacterial cells were incubated with immunoprecipitated HA-PRMT5 in the presence of 1 μl of adenosyl- l -methionine, S- [methyl- 3 H] (1 mCi/ml stock solution, Perkin Elmer). The reactions were performed in the methylation buffer (50 mM Tris pH 8.5, 20 mM KCl, 10 mM MgCl 2 , 1 mM β-mercaptoethanol, and 100 mM sucrose) at 30°C for 1 h and stopped by adding 3 × SDS loading buffer and was resolved by SDS-PAGE. The separated samples were then transferred from the gel to a polyvinylidene difluoride membrane, which was then sprayed with EN 3 HANCE (Perkin Elmer) and exposed to X-ray film.

Immunoprecipitation of FXR1 from UMSCC74B cells

Endogenous FXR1 was purified from control and PRMT5 inhibitor treated 74B cells (2 × 10 6 cells). For immunoprecipitation all steps were carried out at 4°C. The cells were washed with ice-cold 1X PBS buffer followed by cell lysis using 1× cell lysis buffer containing 20 mM Tris (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1% Triton X-100, 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate, 1 mM Na 3 VO 4 , 1 μg/ml Leupeptin and 1 mM PMSF. Lysate was incubated overnight with IP specific FXR1 or IgG anitibody followed by incubation with Dynabeads for 2 h with gentle rotation. After centrifugation, lysate was removed and beads were washed three times with 1× PBS. FXR1 was purified from the antibody-bead complex using glycine buffer (pH 2.0) and the pH of the elute was adjusted to 7.5 using Tris–HCl (pH 7.5). The protein fractions were analyzed by CBB staining and immunoblot.

Structural modelling of G4-RNA binding regions of FXR1

FXR1 region S382-P476 was modelled using Phyre2(46) and Alphafold ( 51 ) servers. As this region was seen to be completely unfolded, two peptide regions corresponding to regions 382–395 and 450–463 were separately used to thread on the FMRP peptide as seen in the PDB structure 5DE5 (in complex with G4-RNA). Mutagenesis and minimization was accomplished in Chimera ( 52 ). All models were minimized by using 1000 cycles of Steepest-descent minimization followed by 50 cycles of conjugate-gradient minimization. All atoms were kept unfixed to allow for free movement. Residue properties were kept in accordance with atom parameters defined by the AMBER ff14SB force field. Finally, hydrogen bonding interaction between G4-RNA and FXR1 peptides was mapped using the generate protocol of PDBsum1(53) hosted by EMBL-EBI. Hydrogen bonds are predicted in accordance with HBPLUS hydrogen bonding potentials developed by McDonald and Thronton ( 54 ). Figures were generated using PyMOL.

Electrophoretic mobility shift assay (EMSA)

Recombinant or endogenous FXR1 protein was assembled onto 30-mer RNA. 0.5 pmol of [y-32P] ATP or 5′ ATTO™ 550 labeled RNA was mock-treated or mixed with recombinant truncated FXR1 (WT or mutants) protein(s) and incubated at room temperature (∼25°C) for 20 min. Reactions were carried out in the final volume of 10 μl of 1X buffer containing 50 mM Tris–HCl pH 7.4, 1 mM MgCl 2 , 0.1 mM EDTA, 150 mM KCl or 150 mM LiCl 2 , 1 mM dithiothreitol (DTT) with 1 U/ul of Murine RNase Inhibitor (NEB), and 100 ug/ml BSA. After incubation, the samples were loaded onto 12% nondenaturing polyacrylamide gel containing 0.5X TBE (Tris–Cl, pH 8.0, Boric acid, EDTA). The electrophoresis was performed at room temperature in 0.5× TBE for 4 h at 125 V. The RNA distribution or shift was visualized by autoradiography after gel drying or imaging at Alexa 546 nm at fluorescence excitation.

Protein thermal shift assay

FXR1 protein stability in presence of different EMSA buffers were tested using the Protein thermal shift assay dye from applied biosystems. Each reaction was carried out in the final volume of 20 μl and the FXR1 protein melt curve was obtained in quant studio 6.0 pro using the parameters specified by the manufacturers. The raw data was analyzed to determine the normalized fluorescence value for the denatured protein using the protein thermal shift software.

eCLIP and data analysis

The eCLIP studies were performed by Eclipse Bioinnovations Inc., according to the published single-end eCLIP protocol ( 55 ). Approximately 20 million UMSCC 74B cells for two biological replicates were ultraviolet crosslinked at 400 mJ cm −2 with 254-nm radiation, cells were scrapped, washed twice with ice cold 1× PBS and stored at –80ºC until it was sent out to Eclipse Bioinnovation Inc. The RBP IP was done using eClip validated FXR1 rabbit monoclonal antibody and the library was prepared according to the published method ( 55 ). Libraries generated using the eCLIP-seq method are sequenced using standard SE50 or SE75 conditions on the Illumina HiSeq 2500 platform in standard single-end formats and peaks were compared with the size matched input (smI) and positive control. Peaks were called using the standard eCLIP processing protocol 0.2, which is available at: https://github.com/YeoLab/eclip .

Immunofluorescence

Optimized multiplex immunofluorescence was performed using the OPAL™ multiplexing method. OPAL™ is based on Tyramide Signal Amplification (TSA) using the Roche Ventana Discovery Ultra Automated Research Stainer (Roche Diagnostics, Indianapolis, IN). Tissues were stained with antibodies against [DAPI, CD3 (1:100), FXR1 (1:100), and PRMT5 (1:100)], and the fluorescence signals were generated using the following fluorophores: [OPAL dyes, Marker + Dye Pairing, Dilution used] (Akoya Biosciences, Marlborough, MA). Slides were imaged at 20X magnification using the Vectra® Polaris™ Automated Quantitative Pathology Imaging System (Akoya Biosciences, Marlborough, MA) and analyzed using inForm® Tissue Analysis Software (v[2.6.0], Akoya Biosciences, Marlborough, MA).

Cell viability and colony formation

Cell viability rate upon UMSCC74B or A549 cells treated with GSK3326593 (GSK593) and GSK3368712 (GSK712) alone or in combination for 72 h is determined using MTT cell proliferation assays (Invitrogen). Briefly, 5 × 103 cells were seeded into each well of a 96-well plate (well area = 0.32 cm 2 ). On the next day, cells were treated with 2 μM of each drug alone or in combination every 24 h, and the medium was replaced with an experimental medium (100 μl). MTT solution was prepared fresh (5 mg/ml in H2O), filtered through a 0.22-μm filter, and kept for 5 min at 37°C. The MTT solution (10 μl) was added to each well post-treatment, and plates were incubated in the dark for 4 h at 37°C. The reaction was stopped using MTT stop solution (10% SDS in 1N HCl) and further incubated overnight at 37°C. The following day the absorbance was measured at A570 nm using a plate reader (Bio-Rad).

Statistical analysis

Data are expressed as the mean ± the standard deviation. Two-sample t-tests with equal variances are used to assess differences between means. Results with P -values <0.05 are considered significant.

TGFβ-induced FXR1 undergoes post-translational modification in cancer cells

Our previous findings demonstrated that overexpressed FXR1 in metastatic oral cancer cells (UMSCC-74A, -74B) and lung adenocarcinoma A549 cells contribute to tumor growth and proliferation ( 16 , 17 ). Silencing FXR1 promotes cellular senescence by activating CDKN1A (p21) and downregulating TERC RNA in both oral and lung A549 cells ( 16 ). Hence, to determine the molecular basis of high FXR1 protein levels in cancer cells, we used A549 lung cancer cells, which show metastatic phenotype under the treatment of cytokine transforming growth factor-β (TGFβ) ( 56 ). The TGFβ-signaling mediated epithelial-to-mesenchymal transition (EMT) is a hallmark of tissue fibrosis, tumor invasiveness, and metastasis ( 57 ). Therefore, to study whether EMT plays a role in high FXR1 protein levels, we used A549 cells and tested their expression under TGFβ. As shown in Figure 1A , TGFβ induced the expression of FXR1 protein with reduced E-cadherin and increased N-cadherin levels (EMT markers). The right panel shows the FXR1 protein quantification. Although FXR1 knockdown (KD) alone showed no changes in the E-cadherin and N-cadherin levels, the addition of TGFβ in FXR1 KD cells facilitated a moderate decrease in E-cadherin and an increase in N-cadherin levels compared to only TGFβ treated cells. This observation is further confirmed by cell morphology changes, in which TGFβ-induced cells exhibit a mesenchymal phenotype and silencing of FXR1 induces senescence (Figure 1B , top panel shows quantitation of senescence). However, the changes in E- and N-cadherin levels from TGFβ treated FXR1 KD cells (Figure 1A ) may signify the changes occurring only in quiescent cells. Next, we tested whether TGFβ-induced FXR1 protein levels are mediated through transcriptional activation of the FXR1 transcript levels. Surprisingly, no difference in mRNA levels of FXR1 was observed in TGFβ-induced cells (Figure 1C ). Hence, we tested whether TGFβ influences the mRNA translation of FXR1 using a polysome gradient assay. The TGFβ-induced A549 cells showed no change in mRNA translation of FXR1 compared to untreated cells (Figure 1D ). These data indicate that high expression of FXR1 in the presence of TGFβ might be associated with post-translational modification (PTM) that may contribute to its protein stability. Therefore, we tested FXR1 protein stability by treating the cells with the protein synthesis inhibitor cycloheximide. As shown in Figure 1E and  F , the TGFβ treated cells showed increased FXR1 protein stability compared to untreated cells, implying that FXR1 may undergo PTM in TGFβ-treated cells. The findings indicate that the molecular basis for overexpressed FXR1 levels in cancer cells is possibly due to PTM, which could influence its oncogenic function.

TGFβ-induced FXR1 undergoes post-translational modification in cancer cells. ( A ) Western Blot analyses show protein regulation by TGF-β treatment (48 h) on A549 cells. GAPDH serves as a loading control. The bar graph on the right side depicts the quantitative value of FXR1 in panel-A western blot. N  = 3. ( B ) Analyses of cell morphology (upper panel) and β-Gal staining (lower panel) of the A549 cells treated with TGF-β and shRNA. The upper panel depicts the quantitative pixel values of β-gal positive cells, an indicator of cellular senescence. ( C ) qRT-PCR of the samples mentioned above (A and B) show that TGF-β only affects the FXR1 protein and does not affect its RNA level. N  = 3. *** P  < 0.0005. ( D ) Polysome profiling of A549 cells with TGF-β treatment compared to control. DNA gel shows the RT-PCR products from serial polysome fractions from control and treated TGF-β samples and analyzed for FXR1 expression in each pulled polysome fraction. ( E ) A549 cells were pretreated with TGF-β or control diluent for 48 h, followed by 5 μM cycloheximide treatment for 0 to 10 h to block protein synthesis. After the treatment, the cells were harvested at the indicated time points and immunoblotted for FXR1, P21 and β-actin (loading control). ( F ) Quantitative analyses of FXR1 protein levels in control and TGFβ treated A549 cells followed by cycloheximide treatment. The results plotted here represent the mean ± SEM of three independent experiments. All the data were defined as mean ± SD and were analyzed by Student's t -test ( n  = 3). *** P  < 0.0005.

PRMT5-mediated arginine methylation promotes the PTM of FXR1

The TGFβ-induced PTM of FXR1 may be carried out by phosphorylation, acetylation, or arginine methylation to promote protein stability of the RGG domain-containing proteins ( 58 ). Hence, we determined whether specific arginine methylation carried out by protein methyltransferases targets FXR1 and promotes its stability in cancer cells. We used PhosphoSitePlus (phosphosite.org) amino acid predictions and selected the methylation sites on specific arginine residues of FXR1. Based on the C-terminal NES and RGG domain amino acid sequences, we chose arginine amino acids 386, 388, 453, 455 and 459 (Figure 2A ) and studied their methylation status and interactions with different methyltransferases. The preferred amino acids are highly conserved between humans and mice and moderately conserved in a known FXR family member, FMRP (Figure 2B ), suggesting that these conserved amino acids may play a role in the biological function of FXR1 in cancer cells by contributing to its stability. To determine the arginine methyltransferase that methylates FXR1, we generated Myc-tagged FXR1 with Arg (R) to Lys (K) mutation of the above residues. We expressed and confirmed the wild-type and R mutant constructs (individually and together) in the human embryonic kidney (HEK) 293T cells (Figure 2C ). PRMT1 is the primary type I enzyme responsible for approximately 80% of total arginine methylation (asymmetric dimethylarginine [ADMA]), whereas PRMT5 is the dominant type II enzyme that generates symmetric dimethylarginine (SDMA) ( 59 ). The expression of both PRMT5 and PRMT1 has been tested in multiple head and neck squamous cell carcinoma (HNSCC) and A549 cells where OHKC (immortalized normal oral keratinocytes) and DOK (dysplastic oral keratinocytes) cells serve as normal and dysplastic cell lines ( Supplementary Figure S1A ). PRMT5 is predominantly expressed across all the cell lines compared to PRMT1. We also found that the levels of FXR1 and PRMT5 increased with TGFβ treatment ( Supplementary Figure S1B ). Hence, we tested the methylation status of wild-type (WT) and mutant FXR1. The cellular lysates from HEK 293T cells transfected with an empty vector and a plasmid expressing Myc-FXR1 (WT) were immunoprecipitated using a c-Myc antibody, separated by sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE), and immunoblotted for both ADMA and SDMA (Figure 2D ). An antibody specific to ADMA failed to detect methylated FXR1, however an antibody against SDMA detect the WT-FXR1 indicated that FXR1 is symmetrically dimethylated at Arg residues. HEK293T cells expressing WT and R386/459K FXR1 were subjected to immunoprecipitation (IP) with Myc-antibody and probed for anti-SDMA antibody. As shown in Figure 2E , the SDMA antibody only reacted to the WT and failed to detect any methylation on FXR1 (R386-459K), confirming the symmetrical dimethylation of these arginine residues in FXR1. Hence, to ensure PRMT5 interacts with methyl Arg residues of FXR1, both WT and R386-459K independently expressing cell lysates were subjected to IP and probed for SDMA, PRMT5, PRMT1 and a positive control FMRP (which interacts with FXR1 through N-terminal Tudor domains) ( 60 ). As shown in the figure, WT FXR1 interacts with SDMA antibody and PRMT5 through the c-terminal NES/RGG box; however, it failed to establish a strong interaction with PRMT1. This finding indicates that PRMT5 targets Arg residues of FXR1 and methylates them. More importantly, Arg residues of R388, R455 and a complete mutation of Arg residues failed to interact with PRMT5, suggesting that these two Arg residues are likely targeted by PRMT5 (Figure 2F and the bottom graph). The direct protein-protein interaction between FXR1 and PRMT5 was further confirmed using overexpressed HA-tagged PRMT5 IP lysates probed for both SDMA and FXR1 in HEK293T cells ( Supplementary Figure S1C ). Finally, we carried out the cycloheximide assay to ensure Arg residues are essential for FXR1 protein stability. Both Myc-tagged stably expressed WT and R386-459K proteins in A549 cells were treated with cycloheximide for designated times (up to 10 h) and tested for FXR1 levels by probing with Myc-Ab. As indicated in Figure 2G , after 10 hours, the WT FXR1 level is comparable to its initial time. In contrast, the mutant protein level after 10 hours was reduced to ∼50% compared to the initial time. This observation indicates that arginine residues at positions R386, 388, 453, 455 and R459 may be essential for FXR1 protein stability, individually or collectively. Thus, these observations demonstrated that PRMT5 interacts with FXR1 and promotes its stability in cancer cells.

PRMT5-mediated arginine methylation promotes PTM of FXR1. ( A ) The protein structure of FXR1 protein has regions marked for its different domains. The C-terminal arginine-glycine-glycine (RGG) RNA-binding domain has the methylated arginine (R) residues marked in the illustration. ( B ) Multiple sequence alignment of the C-terminus of human and mouse FXR1 and FMRP proteins is shown. Secondary structural elements are marked above the sequences, with α-helices depicted as cylinders and β-strands as arrows. The R residues potentially methylated inside the cell have been chosen for the mutation to lysine (K) and are highlighted (yellow). The FXR1 residue numbers are given above the sequence. The numbers in parentheses indicate the length of the sequences shown. ( C ) Immunoblot analyses of WT and mutant Myc-FXR1 protein expressions in HEK293T cells are shown. β-Actin serves as a loading control. ( D ) HEK293 cells expressing empty vector and Myc-tag FXR1 (WT) were used for IP with Myc-tag antibody and probed for SDMA, ADMA and Myc-tag antibodies. The empty vector serves as a control for Myc-FXR1. ( E ) HEK293 cells expressing empty vector, Myc-tag FXR1 (WT), and mutant (R386-459K) were used for IP with Myc-tag antibody and probed for SDMA and Myc-tag antibodies. ( F ) HEK293 cells expressing empty vector, Myc-tag FXR1 (WT), and mutants R386K, R388K, R453K, R455K and R459K were used for IP with Myc-tag antibody and probed for SDMA, PRMT5, PRMT1 and FMRP (positive control). The bottom panel depicts the quantitative value of WT and RGG mutants FXR1 protein interaction with PRMT5. N  = 3. ( G ) A549 cells stably expressing Myc-tag FXR1 (WT) and mutant (R386-459K) were treated with 5 μM cycloheximide treatment for 0 to 10 h to block protein synthesis. After the treatment, the cells were harvested at the indicated time points and immunoblotted for FXR1 and β-actin (loading control). The bottom graph shows the relative FXR1 protein levels with time after cycloheximide treatment. All the data were defined as mean ± SD and were analyzed by Student's t -test ( n  = 3). * P  < 0.05.

Silencing PRMT5 reduces FXR1 and cell growth in HNSCC cells

PRMT5 is the primary enzyme responsible for arginine SDMA of target proteins and it prefers the consensus arginine- and glycine-rich regions known as RGG/RG motifs ( 61 ). PRMT5 targets numerous RGG domain-containing proteins, and inhibiting PRMT5 decreases target protein levels via demethylation ( 61 , 62 ). However, PRMT1 has been shown to carry out protein methylation without PRMT5, indicating a redundancy in the activation of protein methylation by these two methyltransferases ( 63 ). As a result, we investigated whether silencing PRMT5 and PRMT1 affected FXR1, FXR2 and FMRP levels in oral and lung cancer cells. As shown in Figure 3A and the right graph panel, we used two guide RNAs (CRISPR/Cas9) to knockout PRMT1 and PRMT5 in oral cancer cells (lung cancer cells, Supplementary Figure S2A ), and only PRMT5 deletion reduced FXR1 levels but not FXR2 (which lacks the RGG domain), as previously described ( 16 ). Interestingly, PRMT1-silenced cells did not change the protein levels of FXR1 or FXR2, indicating that FXR1 may be a direct substrate of PRMT5 in oral cancer cells. Furthermore, we could not detect the protein FMRP (data not shown), which is not expressed in oral or lung cancer cells.

Genetic and small-molecule inhibition of PRMT5 reduces FXR1 and cell growth in HNSCC cells. ( A ) The immunoblot shows two independent guide RNA-mediated knock out (KO) of PRMT1 and PRMT5 in UMSCC74B oral cancer cells. β-Actin serves as a loading control. Quantitative protein levels of FXR1 and FXR2 from three independent experiments are shown as a bar graph (right panel). ( B ) The panel depicts the colony-forming efficiency from clonogenicity assays of UMSCC74B cells treated with indicated drugs and DMSO for 72 h. ( C ) MTT analysis of cell viability in UMSCC74B cells treated with indicated drugs and DMSO for 72 h. Data presented as the mean ± SD of three independent experiments. ( D ) UMSCC74B cells were treated with PRMT5i and PRMT1i (1.5 μM) for 72 h. RNA extraction followed by qRT-PCR was done to determine the relative mRNA levels of FXR1, PRMT5, PRMT1 and p21. All the data were defined as mean ± SD and were analyzed by Student's t -test ( n  = 3). *** P  < 0.0005. ( E ) Immunoblot analysis of cell extracts obtained from UMSCC74B cells treated with PRMT5i and PRMT1i for 72 h. GAPDH serves as a loading control. The upper bar graph shows the quantitative analyses of FXR1 expression upon treatment. ( F ) Immunoblot analyses of FXR1, comparing FXR2 and PRMT5 levels in UMSCC74B cells upon PRMT5i treatment for 72 h. β-actin served as a loading control. ( G ) Endogenous FXR1 was purified from UMSCC74B control and PRMT5i (2 μM) treated cells using FXR1 specific antibody and mouse IgG (negative control) antibody. Purified protein fractions were analyzed by 10% SDS-PAGE followed by CBB staining. The bottom panel represents the immunoblot confirmation of FXR1 protein obtained from IP. ( H ) Estimating methylation status of endogenous FXR1 purified from UMSCC74B cells treated with PRMT5i (2 μM). Immunoblot was probed with FXR1 and SDMA antibody, a marker of protein methylation. ( I ) UMSCC74B cells were treated with and without PRMT5i for 72 h, followed by treatment with 5 μM cycloheximide for 0 to 8 h to block protein synthesis. After the treatment, the cells were harvested at the indicated time points and immunoblotted for FXR1 and β-actin (loading control). The bottom graph shows the relative FXR1 protein levels with time after cycloheximide treatment. N  = 3. All the data were defined as mean ± SD and were analyzed by Student's t -test * P  < 0.05.

Based on the effect that PRMT5 had on FXR1 levels, we wanted to see if inhibiting PRMT5 demethylated FXR1 and regulated its actions in cancer cells. GlaxoSmithKline (GSK) has found that both the PRMT1 inhibitor GSK3368712 (GSK712) and the PRMT5 inhibitor GSK3326593 (GSK593) have anti-tumor effects in a variety of cancer cell lines, with the exception of HNSCC ( 64 ). To investigate the efficacy of PRMT5 inhibition, we treated oral and lung cancer cells with single and combined PRMT5 and PRMT1 inhibitors (PRMT5/1i). The combination treatment with PRMT5/1i resulted in considerably reduced colony formation (Figure 3B and S2B) and cell growth (Figures 3C and S2C) in both cell lines. Next, we investigated the capacity of PRMT5/1i to inhibit FXR1 mRNA transcript and protein levels in cancer cells. Following the treatment described above, the mRNA and protein levels were measured in the UMSCC74B cells. In addition, we also measured the p21 levels because, FXR1 silencing was already known to regulate p21 mRNA levels ( 16 ). PRMT5i treatment had little effect on FXR1 mRNA, but it elevated p21 levels significantly in oral cancer cells (Figure 3D ). This finding suggests that FXR1 remains unchanged at the mRNA level. However, demethylation by PRMT5i could affect FXR1 protein and increase p21 levels. In addition, we checked the protein levels of FXR1 and p21 to ensure the inhibitor's effectiveness. As Figures 3E and  F indicated, PRMT5 inhibition affected FXR1 but not FXR2 protein levels. Interestingly, a significant rise in p21 levels was also found in PRMT5-inhibited cells, implying that unmethylated FXR1 may be dormant in both oral and lung cancer (Figure S2D and S2E) cells. Interestingly, inhibiting PRMT1 and PRMT5 increased PARP cleavage, which can be attributed to the cell death as demonstrated by the inability to form colonies (Figure 3B ). Next, to confirm our observation that inhibiting PRMT5 methyltransferase activity reduces Arg methylation and destabilizes FXR1, we employed endogenous FXR1 isolated from control and PRMT5i-treated UMSCC74B cells (Figure 3G ). The purified fraction was tested with the SDMA antibody, which is a marker for PRMT5 activity. Our findings demonstrated that the inhibitory action of PRMT5 failed to methylate FXR1 in vivo (Figure 3H ). To investigate the effect of demethylation on FXR1 protein stability, we treated the cells with cycloheximide in PRMT5 inhibited UMSCC74B cells. The time-dependent experiment demonstrated that FXR1 protein stability is significantly reduced in PRMT5 inhibited cells, in which FXR1 is demethylated (Figure 3I and bottom panel). In addition, we wished to test whether silencing the activity of PRMT5 alters the localization of FXR1 in cancer cells. As shown in Supplementary Figure S2F , there is no change in FXR1 distribution in the cells under PRMT5 silencing condition, demonstrating that demethylation of FXR1 did not alter its cellular localization. These findings clearly showed that FXR1 is dependent on PRMT5 for its methylation and stability, and that reducing FXR1 methylation promotes p21 levels and preventing the cancer cell growth.

Arginine amino acids are essential for FXR1 to bind to G4-RNA sequences

Previous studies have shown that arginine residues in the FMRP RGG box are required for G-quadruplex (G4) RNA binding ( 19 , 65 , 66 ). As a result, we investigated whether arginine residues in FXR1 have a similar role in binding to the p21 mRNA fragment that contains a canonical G4-RNA sequence. The protein structure of FXR1 is less well-established than that of the FMRP C-terminal domain secondary structure ( 65 ). It is also unclear how FXR1 identifies G4-RNAs and which amino acids are required for binding to G4-RNAs. To assess the relevance of these arginine residues in FXR1-G4-RNA binding, we created a 30 nucleotide RNA (sequence excised from human P21 3′UTR, seg1 ( 17 )) with a G4-RNA motif (Figure 4A ). We have previously demonstrated that FXR1 binds to G4-enriched fragment of the p21 3′UTR ( 16 , 17 ). To analyze the structural workings of various arginine binding capacities, we modeled FXR1 S382-P476 using the Phyre248 and Alphafold49 servers ( 53 ). Because this region lacked any secondary structural elements, we identified two nodes for threading into G4-RNA-bound structures using the FMR1 peptide as a template (PDB ID:5DE5) ( 67 ). Here, Node1 is defined between amino acids 382- 395 (contains R386 and R388), and Node2 entails amino acids 450–463 (includes R453, R455 and R459) (Figure 4A ). Our modeling analysis showed that Node1 formed a complex with G4-RNA using R386 when threaded in either direction (from N to C terminus, Figure 4A or C to N terminus, Supplementary Figure S3A ). Specifically, R386 formed stable hydrogen bonds with G29, C30, C5, and G7 when threaded from the N to C terminus and the C to N terminus, respectively (Figure 4A ). In comparison, Node2 could only be threaded from the N to C terminus, where C to N terminus threading was disallowed due to stearic clashes of the peptide with the G4-RNA. Hence, modeling studies indicate that these two nodes are the predominant interactors of G4-RNA. Finally, we sought to determine whether FXR1 arginine amino acids are critical for binding with G4-RNA. To begin, we cloned a protein sequence comprising FXR1’s NES and RGG (S382-P476) domains in the pGEX-6P1 vector, then altered the arginine residues (R to K) and purified it using the GST-affinity purification technique ( Supplementary Figure S3B ). The in vitro methylation analysis showed that PRMT5 successfully methylated WT FXR1 but failed to adequately methylate the arginine mutants R386, R388, R45’, R459 and R386-459K (Figure 4B ), demonstrating that PRMT5 methylates arginine at these specific positions on FXR1. Further, the recombinant WT and arginine mutant FXR1 proteins were subjected to an electrophoretic mobility shift assay (EMSA) with a radiolabeled 30-mer/ fluorescently labeled G4-RNA substrate. The resulting EMSA studies showed that WT FXR1 binds with G4-RNA at a dissociation constant (Kd) of 25 nM; however, most R to K (arginine to lysine) mutants of FXR1 bind poorly with G4-RNA with high K d and the R386K, and R386/459K fails to interact with the G4-RNA (Figure 4C and  D ). To validate the specificity of FXR1 to the G4 region, we used LiCl2 instead of KCl as a metal ion in the EMSA buffer and examined the binding. It has been shown that potassium stabilizes the G4-RNA over lithium ( 68 ). As shown in Figure 5A (right panel, binding curve), potassium ions enhance G4-RNA binding to FXR1. Our results showed that lithium failed to retain the G4-RNA structure and could not bind to FXR1, indicating that FXR1 prefers G4-RNA configurations. However, it is critical to demonstrate that LiCl2 does not affect FXR1 protein stability and merely destabilizes the G4 structure. As a result, we performed the protein thermal shift assay (PTSA) as described in the experimental methods. We observed that LiCl2 had no negative influence on protein stability and maintained the same melting temperature as the sample buffer containing KCl. In addition, the same trend was observed when we used the samples with RNA between different buffers (Figure 5B ). To validate our in vitro observation, we conducted the EMSA with endogenous FXR1 protein purified from UMSCC74B control and PRMT5i cells using FXR1 specific antibody. As shown in Figure 5C and the bottom panel binding curve, the endogenous FXR1 exhibited a similar binding affinity to G4-RNA in control FXR1 whereas the binding was not significant in the FXR1 purified from PRMT5i cells. Furthermore, the endogenous FXR1 lost the RNA binding when we used LiCl2 as previously seen with the recombinant protein (Figure 5D and bottom graph). Thus, our findings provide compelling evidence that FXR1 preferentially binds to G4-RNA via its selective arginine residues.

Arginine residue in the NES and RGG domain of FXR1 are essential to bind with G4-RNA sequences. ( A ) The sequence and plausible structure of a 30-mer RNA is used for EMSA assays. The energy-minimized model of FXR1 region 382–395 is threaded on the structure of FMR1 with G4-RNA. When threaded in either direction, R386 makes strong hydrogen bonds with G4-RNA nucleotides and backbone phosphates. Node assembly to investigate G4-RNA binding of FXR1 region 382–476. Peptides from regions 382–395 and 450–463 were used to model them with G4-RNA. Interacting arginine residues that show sensitivity to methylation are highlighted. ( B ) In vitro methylation assay was performed with recombinant GST-FXR1 protein purified from bacterial cells and Myc beads bound with PRMT5/MEP50. The methylation assay was carried out in the presence of 3 H-SAM. The binding was performed at 4°C for 4 h, incubated with or without PIP3 (20 μM), and subjected to immunoblot analyses. PRMT5.MEP50 proteins were purified from HEK293 cells. The Ponceau stain below serves as a loading control for the immunoblot above. ( C ) EMSA with 5′-labeled 30-mer RNA, recombinant FXR1 (S382-P476) WT, and respective arginine mutant proteins. 0.5 pmol of [y-32P] ATP-labeled RNA was mock-treated or mixed with increasing concentrations of recombinant WT and mutant FXR1 proteins and incubated at 25°C for 20 min. Free RNA and RNP complexes are shown in the figure. ( D ) The binding curves and affinity constants are shown for each recombinant protein-RNA complex.

PRMT5-dependent FXR1 methylation is required for G4-RNA binding in HNSCC. ( A ) EMSA was performed as mentioned above with 5′ ATTO 550 labeled 30-mer RNA using recombinant WT FXR1 protein in EMSA buffer containing 150 mM KCl/LiCl 2. The RNA-protein interaction was analyzed using 10% native PAGE gel and visualized using typhoon FLA 7000 at 546 nm. The right panel shows the binding curves of EMSA. B. Protein thermal shift assay was used to screen for the effect of KCL/LiCl 2 on FXR1 using Sypro Orange. Data from protein thermal shift software show the Boltzmann (upper) and derivative (lower) melt profiles of FXR1 with or without different buffers (KCL/LiCl 2 ) , and with RNA (sample used for EMSA). Data were collected as mentioned in the methods. The median derivative T m and Boltzmann derivative T m are represented in black and green vertical lines, respectively. ( C ) EMSA was performed as indicated above with endogenous FXR1 from UMSCC74B cells with and without PRMT5 inhibitor treatment. The bottom panel represents the binding curves of EMSA. ( D ) EMSA was performed as indicated in above in a buffer containing 150 mM KCl/ LiCl 2. The bottom panel represents the binding curves of EMSA.

The RNA-binding landscape of FXR1 demonstrates its possible role in RNA regulation

In our recent findings ( 16 , 17 ), we demonstrated that FXR1 binds to the G4-specific region of p21 and degrades the mRNA in an miR301a-3p-dependent manner. In addition to our findings, others have found that FXR1 targets multiple mRNAs, including p21, in mouse C2C12 cells ( 69 ). Hence, we decided to determine the global analysis of FXR1-associated transcripts using enhanced crosslinking and immunoprecipitation (eCLIP) ( 55 ). As described, the UMSCC74B cells were subjected to UV-cross linking and IP with FXR1 for eCLIP analysis. The eCLIP followed by RNA-seq analysis (GEO: GSE252916, reviewer token- kfwrseckrlepjet), data show that FXR1 binds to diverse locations (5′ and 3′ UTR, coding and intergenic RNA regions) of several target RNAs, accounting for 21000 reproducible peaks in both biological replicates ( Supplementary Data 1 ). Further analysis revealed that 96% of FXR1 binding peaks were matched to coding sequences (Figure 6A ). However, FXR1 has also displayed a high RNA binding preference for 5′, coding, and 3′ UTR sequences (Figure 6B and the inset). Hence, both 5′ and 3′ UTR sequences were taken for further analysis due to their role in mRNA turnover and translation functions. We focused on 3′UTR sequences over 5′UTR due to their direct role in RNA turnover functions. Our data indicate that 1.86% of eCLIP peaks was also mapped on the 3′ UTR, that are highly enriched with top targets such as MAP1B, HUWE1, DYNC1H1, AHNAK2, AHNAK and UBR4. The FXR1 binding RNA sequence motifs were identified using HOMER12 de novo motif analysis ( http://homer.ucsd.edu/homer/motif/ ). Based on their P -value, the resulting motif analysis indicates that the most enriched peaks displayed high G-rich sequences (Figure 6C and Supplementary Data 2 ). Based on their G4-rich sequences and binding preference to top targets, we mapped the FXR1 binding to the respective mRNA targets using the hg19 genome browser as indicated by eCLIP analysis. As shown in Figure 6D , FXR1 IP samples showed significant enrichment of target mRNAs compared to input samples, indicating that FXR1 preferentially binds to selective regions of mRNAs. Next, we intended to determine whether the enriched mRNAs contain canonical G4-RNA sequences in their 3′UTR ( Supplementary Data 3 ). We used a G4 mapper ( 70 ) to map the potential G4 sequences in the most enriched peaks for the top FXR1 RNA targets. Surprisingly, most of the FXR1’s identified RNA targets contain numerous G4 sequences spanning from the 5′UTR to the 3′UTR ( Supplementary Data 4 ). Altogether, the findings from this eCLIP analysis further confirm our earlier in vitro and in vivo investigations, indicating FXR1 has a relatively higher affinity for binding towards G4-RNA sequences in the mRNA. Moreover, the gene ontology (GO) enrichment analysis revealed that FXR1 interacting mRNA encoding proteins are associated with cell cycle, phosphatidylinositol signaling, ubiquitin-mediated proteolysis, and nucleocytoplasmic transport ( Supplementary Data 5 ). These findings suggest that the FXR1-RNA network-associated biological processes facilitate cancer cell growth and proliferation.

RNA binding landscape of FXR1 by eCLIP and RNA seq. ( A ) The pie chart depicts the distribution of the FXR1 eCLIP peaks in the human genome analyzed from two biological replicates. UTR-untranslated region; CDS coding sequence. The data was considered with the cut-off values of peak log 2 fold enrichment ≥3 and P -value ≤0.001. ( B ) The binned FXR1 eCLIP peak coverage across all expressed genes in UMSCC74B cells. The inset represents the metagene plots of the normalized average number of peaks mapped to specific genomic regions. The 5′UTR, CDS and 3′UTR of each gene are split into 13, 100 and 70 bins, respectively. ( C ) Top ten most significantly enriched de novo sequence motifs in the FXR1-binding peaks using HOMER12. The percentage of peaks containing the discovered motifs and the p-values of the motifs calculated by a binomial test against the random genomic background was shown. ( D ) Integrated genome viewer (IGV) browser tracks the FXR1’s eCLIP peaks of top targets (based on pvalue and log 2 fold change) spanning the genomic loci of AHNAK2, MAP1B, HUWE1, UBR4, DYNC1HI and AHNAK. Detailed information about all significantly enriched eCLIP peaks can be found in Supplementary Data-1 .

Multifaceted gene regulatory roles of FXR1 in HNSCC cells

To interrogate the oncogenic functions and gene signatures essential for cancer cell growth and proliferation, we performed an RNA-seq by silencing FXR1 and PRMT5 separately using shRNAs and analyzed the high-throughput sequencing data. The silencing effect of shPRMT5 was confirmed using immunoblot ( Supplementary Figure S4A ). For this analysis, we used total RNA isolated from the WT, FXR1 KD and PRMT5 KD cells, and subjected them to bulk RNA sequencing analysis (FXR1:GSE212760, reviewer token-ypqfmuiapxetdyh, PRMT5: GSE256352, reviewer token-mdapmcyijzgjdud). Bioinformatics analyses identified several differentially expressed genes based on a threshold of q  ≤ 0.05 (FDR 5%) for statistical significance and a log-fold expression change with an absolute value of at least 1. Principal Component Analyses (PCA) plot depicts the gene expression variance that is exhibited between KD samples of FXR1 and PRMT5 ( Supplementary Figure S4B ). The heat map of differentially expressed genes (DEGs) identified in the KD and control samples is depicted in Figure 7A , and S4C showed PRMT5’s DEGs. The next bar chart and the dot plot depicts the functional enrichment of DEGs from diverse biological processes in FXR1 KD (Figure 7B ) and PRMT5 KD respectively ( Supplementary Figure S4D , upregulated pathways S4E down regulated pathways). The x-axis corresponds to the number of genes in the functional ontology. The functional enrichment of FXR1 DEGs indicated top 6 hallmark gene sets obtained from the MSigDB database (Figure 7C ), demonstrating its biological importance relating to interferon pathways. More importantly, Gene Set Enrichment Analysis (GSEA) predictions, and we identified 22 pathways that FXR1 significantly impacts. The GSEA pathway further shows that several cancer pathways are negatively affected, and anti-cancer pathways are positively regulated. Graphical representation of the rank-ordered gene lists for Interferon Alfa Response and P53 Pathways hallmark gene sets (Figure 7D ). The heat-map of FXR1 KD RNA seq depicts the expression levels of various top eCLIP targets according to the highest fold change and pvalue (Figure 7E ). While analyzing the RNA-seq data of FXR1 knockdown, we observed changes in multiple pathways associated with cancer. However, examined the significance of these findings concerning the eCLIP targets of FXR1. Next, to investigate the expression of regulated mRNAs (DEGs) connected with FXR1 (eClip) under FXR1 or PRMT5 KD circumstances, we identified the mRNAs that are present in all three conditions. Specifically, 130 genes showed increased expression (Figure 7F ) and 190 genes showed decreased expression (Figure 7G ). The GO enrichment of FXR1 eCLIP target expression that is altered under FXR1 and PRMT5 KD conditions is found to be mostly enhanced in nucleic acid binding, and helicase activities and reduced in enzyme binding and regulatory activity ( Supplementary Figure S4F and S4G ). To validate the changes in FXR1-related transcripts under both KD conditions, we examined the expression of important gene targets that are tightly bound to FXR1. According to the data presented in Figure 7H , the qRT-PCR validation of selective FXR1 targets showed a predominant decrease in expression in both FXR1 and PRMT5 KD cells. Surprisingly, TCGA database analyses of HNSCC patient tissues have revealed the FXR1 top targets are altered at the mRNA level, indicating the targets may exert an oncogenic role in HNSCC ( Supplementary Figure S4H ). Moreover, the GO enrichment analyses revealed that the 18 highest-ranking mRNA targets of FXR1 are majorly involved in nitrogen metabolism, microtubule formation, axonal control, and cell proliferation (Figure 7I ). This suggests that FXR1 can bind to and stabilize these transcripts, hence possibly promoting the growth and proliferation of cancer cells. The results further indicate that the FXR1-PRMT5 axis could have a significant impact on the development of cancer through the control of the above-mentioned biological process.

FXR1 and PRMT5-dependent altered gene signatures in HNSCC cells. ( A ) Heat map of significantly differentially expressed genes identified between FXR1 KD and control samples. Rows show Z scores of normalized, log2-transformed values from differentially expressed genes (FDR < 0.05). Dendrograms depict Pearson correlation clustering of samples. ( B ) Bar plot representing the functional enrichment of FXR D1 DEGs of the top 6 genes ontology biological process (BP). The X-axis corresponds to the number of genes in the functional ontology. The Y-axis shows the top 5 functional ontologies ranked by significance. Gradient color depicts the FDR value (red = most significant, blue = least significant). ( C ) Bar plot representing the functional enrichment of FXR D1 DEGs of the top 6 hallmark gene set from MSigDB database (FDR < 0.05). The X-axis corresponds to the normalized enrichment score based on GSEA analysis. ( D ) Graphical representation of the rank-ordered gene lists for Interferon Alfa Response (NES = 3.29, FDR = 1.24e-27) and P53 Pathways (NES = 1.50, FDR = 1.27e-02) hallmark gene sets. ( E ) Heat map for the top FXR1 eCLIP RNA targets shows differential expression profile in UMSCC74B control and FXR1 KD cells. ( F ) Venn diagram represents the FXR1 eCLIP targets commonly up-regulated in both FXR1 KD and PRMT5 KD conditions. ( G ) Venn diagram represents the FXR1 eCLIP targets commonly down-regulated in both FXR1 KD and PRMT5 KD conditions. ( H ) Quantitative real-time PCR validation of top eCLIP targets having the highest fold-change and P -values compared to the size-matched input. The results plotted here represent the mean ± SEM of three independent experiments. All the data were defined as mean ± SD and were analyzed by Student's t -test ( n  = 3). *** P  < 0.0005. ( I ) The bar graph represents the GO enrichment analyses of the top eighteen FXR1 eCLIP targets.

Overexpressed PRMT5 and FXR1 predict poor patient outcomes and show clinical significance

Others have reported that PRMT5 is overexpressed in HNSCC ( 71 ), and inhibition of PRMT5 by EPZ015666 (GSK3235025) reduces H3K4me3-mediated Twist1 transcription and suppresses the carcinogenesis and metastasis of HNSCC ( 72 ). PRMT5 ( 73 ) and FXR1 ( 14 , 16 , 21 ) are overexpressed in multiple cancers, but combinatorial expression changes in cancers have never been reported. In addition, we tested the mRNA level changes of PRMT5 and FXR1 in The Cancer Genome Atlas (TCGA) HNSCC and lung adenocarcinoma data sets. As shown in the survival plot the overexpressed PRMT5 and FXR1 (SD > 1) alone ( Supplementary Figure S5A and S5B ) or in combination (Figure 8A ), lead to poor patient survival in HNSCC and lung cancer patients. FXR1 protein is overexpressed in oral tumors compared to normal tissue and colocalized with PRMT5, demonstrating that both proteins contribute to an oncogenic phenotype (Figure 8B ). Hence, targeting PRMT5 to modulate FXR1 functions is significant and may provide a unique anti-tumor response for HNSCC and lung adenocarcinoma patients.

PRMT5-dependent FXR1 preferentially targets oncogenes and alters its expression in HNSCC. ( A ) Kaplan–Meier plots of overall survival of stage HNSCC patients ( n  = 522) stratified by FXR1 and PRMT5 mRNA expression (SD > 1). The log-rank P value and the number of cases per group are shown. ( B ) Optimized multiplex immunofluorescence showing the expression of FXR1 and PRMT5 in human HNSCC tumor and normal adjacent tissue samples. DAPI and CD3 staining was done for the nucleus and tumor markers. ( C ) Model represents the methylation dependent regulation of FXR1 and its RNA targets to promote or inhibit the tumor cell proliferation.

The results of our study have revealed that FXR1 is a target of PRMT5 for arginine methylation. Furthermore, our data indicate that arginine methylation occurs explicitly in the NES and RGG box domains of FXR1 in cancer cells. Chromosome 3q amplification in lung and oral cancer patients leads to an increase in FXR1 mRNA levels and exert oncogenic properties ( 14 , 16 ). This study has identified and added a new feature that FXR1 protein undergoes post-translational modification by PRMT5-mediated arginine methylation, which enhances the stability of FXR1 protein (Figure 1 ). Our findings also show that PRMT5 directly adds a dimethyl group to FXR1 arginine residues in cancer cells. Based on the FXR1-PRMT5 protein-protein interaction and methylation status, the residues R388K and R455K demonstrated a lack of interaction with PRMT5 compared to WT, demonstrating that these residues might have a strong preference to get methylated by PRMT5. The improved stability of FXR1 protein may be attributed to the arginine residues R388 and R455, which exhibited robust interactions with PRMT5 (Figure 2A ). Moreover, we have also demonstrated that FXR1 demethylation through inhibition of PRMT5 affected the protein stability and reduced the cancer cell proliferation (Figure 3I ).

Post-translational modifications, including arginine methylation, regulate protein functions and this modification requires approximately 12 ATPs to add a single methyl group to a protein ( 78 ). Methyl groups added to the amino groups of amino acid side chains often increase steric hindrance and reduce hydrogen bonds by replacing the amino hydrogens ( 79 ). For example, hnRNP A1 is methylated by PRMT5 on two residues, R218 and R225, which facilitates the interaction of hnRNP A1 with IRES RNA to promote IRES-dependent translation ( 82 ). Arginine methylation of different proteins, including FXR1 family protein, FMRP, affects protein–RNA interactions, protein localization, and protein-protein interactions ( 25 ). Studies have shown that the RGG box of FMRP, is known for recognizing G-quadruplex RNAs ( 81 ) and arginine residues are highly favored when it comes to RNA binding ( 80 ). Moreover, published findings showed that the folding of G4-RNAs in vitro is similar to in vivo conditions ( 83 ). For example, the sequences we used from p21 3′-UTR are folded as a G4 (Figure 4B ) to bind with FXR1 properly. Additionally, the studies have indicated that G4-RNA must be efficiently folded to interact with protein FMRP ( 84 ). Due to the close proximity of FXR1 arginine residues spanning NES and RGG motifs, there is a likelihood that PRMT5 methylates multiple arginine residues at a given time and alter the protein stability and function of FXR1. Further, this methylation also facilitates FXR1 to bind with G4-RNAs and control their expression through a potentially novel mechanism, which requires further exploration. Based on our biochemical structure prediction, we have used a 30-base RNA that forms a G4 structure to show the binding affinity of FXR1 arginine residues. Both in vitro and in vivo assays show that arginine residues present in the NES (R386 and R388) and RGG domain (R453, R455, R459) of FXR1 are essential for binding with G4-RNAs (Figures 4 and  5 ). Subsequent in vitro binding experiments using arginine mutants demonstrated that changes in arginine residues of FXR1 lead to decreased affinity for G4-RNA. Interestingly, the binding study employing the endogenous FXR1 further validated our in vitro observations and confirmed the interplay between FXR1 and PRMT5 that is vital for G4-RNA binding by FXR1 (Figure 5 ). To further prove our claim that FXR1 prefers G4-RNAs, we used LiCl2 to destabilize the G4-RNAs and see the effect through binding studies. It has been shown that structural analysis of G4-RNA with various metal ions favors potassium as a stabilizing agent over lithium ( 68 ) (Figure 4 ). Interestingly, in the presence of potassium FXR1 strongly interact with G4-RNA, but lithium destabilizes the G4-RNA structure and disrupts the binding with FXR1 (Figure 5 ), suggesting that FXR1 may prefer a noncanonical G4-structure to interact with the RNA. Previous findings from the Darnell laboratory also stated that FMRP binds with G4-RNAs and represses mRNA translation in neuronal cells ( 74 ). Thus, methylation of the arginine residues can either help increase or decrease the RNA binding capacity of the methylated protein.

Our published findings show that FXR1 specifically targets the G4-rich regions of p21 mRNA and TERC long non-coding RNA to control their expression in oral cancer cells ( 16 ). Deleting the G4-region of p21 mRNA specifically did not interact with FXR1 in cancer cells, indicating that FXR1 prefers G4-sequences in the 3′UTR to regulate the expression of target genes. FXR1 facilitates the degradation of p21 mRNA at the molecular level by enlisting miR-133a-3p and PNPase to induce instability ( 17 ). The mechanism by which FXR1 binds to and stabilizes TERC RNA through interaction with the G4-region is not well understood. TERC RNA may not have miRNA binding sites, hence FXR1 interaction could potentially enhance TERC stability rather than destabilize it. Darnell group showed that FMRP interacts with the coding region of many mRNAs associated with autism spectrum disorders ( 75 ). Interestingly, FMRP is known to interact with G4-RNA sequences located at the 3′UTR, influencing the localization and translation of target mRNAs ( 77 ). It is also vital to show in this study that FXR1 prefers the G4-mRNAs in head and neck cancer cells, mostly localized in the cytoplasm. Nevertheless, our eCLIP data clearly demonstrate that FXR1 interacts with and regulates the target mRNAs both in a positive and negative manner in cancer cells (Figure 6 ). Utilizing the eCLIP analysis and FXR1 KD gene signature analysis, we have successfully demonstrated that the differential gene expression is mediated by FXR1. According to the eCLIP motif analysis, FXR1 can bind to both G- and U-rich sequences. The FXR1 target mRNA encoding proteins include AHNAK, AHNAK2, MAP1B, HUWE1, and DYNC1H1, as depicted in Figure 6 , enriched with G4-sequences. Our data also show that FXR1 targets the coding regions, 5′UTR, and 3′UTR of key genes involved in microtubule filaments, potentially linked to cancer progression (Figure 7 ). For instance, MAP1B, a microtubule filament protein, the prominent target of FXR1, is also targeted by FMRP and is associated with autistic spectrum disorder and autophagy ( 76 ). Therefore, establishing the connection between FXR1 and the microtubule-associated gene network would reveal the crucial role of FXR1 in cancer cells. Further experimental strategies are needed to determine if FXR1 binds to non-G4 RNAs and acts as a repressor or promoter of their mRNA turnover and translation in cancer cells.

Our RNA-seq and eCLIP analysis showed that silencing FXR1 can have both cancer positive and negative effects on gene expression, suggesting that the recognition of G4-region may influence mRNA turnover regulation. The contrasting roles of FXR1 in mRNA stability and destabilization considering the G4-structural features need to be investigated further in cancer cells. Together, our results show that arginine methylation may influence its target mRNAs having preference towards G4 enriched sequences to regulate its gene expression in cancer cells. FXR1 shows high methylation levels and can have more preference to bind G4-RNAs containing regulatory signals for generating proteins that are crucial for encouraging tumor growth. Thus, the current results indicate a straightforward function of FXR1 in cancer cells that may pave the way for targeting the NES/RGG box for therapeutic intervention to elucidate the regulation of tumor suppressors in cancer cells.

Collectively, our data unambiguously demonstrated the molecular interaction between PRMT5 and FXR1 by the impartial techniques. As demonstrated in Figure 8 , head and neck tumors have limited survival and poor outcomes due to the overexpression of FXR1 and PRMT5. The rationale behind integrating FXR1 and PRMT5 inhibitors to improve clinical outcomes is presented in our work. More importantly, as our model illustrates (Figure 8C ), we showed that PRMT5-activated FXR1 is intricate in controlling the mRNA expression of its targets, playing both tumor-activating and tumor-suppressive roles. Therefore, further research is required to fully comprehend FXR1’s involvement in mRNA synthesis and turnover in cancer cells, leading to cancer growth and proliferation.

The data underlying this article are available in the Gene Expression Omnibus, and can be accessed under accession codes GSE252916, GSE212760 and GSE256352. Further data is available in ModelArchive at https://modelarchive.org/doi/10.5452/ma-epklf .

Supplementary Data are available at NAR Online.

This work was supported by the National Institutes of Health NIH Grant R01 DE030013 and R21DE032461. Supported in part by the Translational Science Shared Resource, Hollings Cancer Center, Medical University of South Carolina (P30 CA138313). This study received funding from the UNM Comprehensive Cancer Center assistance Grant NCI P30CA118100. The study also utilized the Analytical and Translational Genomics Shared Resource at University of New Mexico, which receives financial assistance from the State of New Mexico.

National Institutes of Health [R01DE030013, R21DE032461]. Funding for open access charge: NIH.

Conflict of interest statement . None declared.

Choi   P.S. , Thomas-Tikhonenko   A.   RNA-binding proteins of COSMIC importance in cancer . J. Clin. Invest.   2021 ; 131 : e151627 .

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Role played by MDSC in colitis-associated colorectal cancer and potential therapeutic strategies

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• Published: 08 May 2024
• Volume 150 , article number  243 , ( 2024 )

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• Kang Wang 1 ,
• Yun Wang 2 &
• Kai Yin 3  

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Colitis-associated colorectal cancer has been a hot topic in public health issues worldwide. Numerous studies have demonstrated the significance of myeloid-derived suppressor cells (MDSCs) in the progression of this ailment, but the specific mechanism of their role in the transformation of inflammation to cancer is unclear, and potential therapies targeting MDSC are also unclear. This paper outlines the possible involvement of MDSC to the development of colitis-associated colorectal cancer. It also explores the immune and other relevant roles played by MDSC, and collates relevant targeted therapies against MDSC. In addition, current targeted therapies for colorectal cancer are analyzed and summarized.

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Introduction

Colorectal cancer (CRC) is the third most prevalent cancer worldwide and one of the leading causes of cancer-related deaths. With economic development and lifestyle changes, its incidence has been increasing year by year (Morgan et al. 2023 ), and studies have shown that the incidence of CRC has increased in people under 50 years of age in the past few decades (Spaander et al. 2023 ), with a shift towards diagnosis at a younger and more advanced stage (Siegel et al. 2023 ), as the population continues to age, the global cancer burden is expected to increase, making it a global public health problem that should not be underestimated. Previous studies have shown that people with inflammatory bowel disease (IBD) have an increased chance of developing colorectal cancer over time, the development of colorectal cancer, particularly colitis-associated colorectal cancer, is strongly associated with a cumulative inflammatory burden (Yvellez et al. 2021 ). Recent studies have shown that MDSCs in the immune system have a major impact on colorectal cancer progression, and together with many other immune cells, they jointly stimulate the proliferation of tumor cells, and participate in local angiogenesis and distal tumor metastasis.

In inflammatory bowel disease (IBD), intestinal inflammation and massive infiltration of bone marrow and lymphocytes are the main pathological features. It has been demonstrated that dendritic cells (DCs) and macrophages play a crucial role in controlling the development of pro-inflammatory lymphocytes, such as helper T cells and Th17 cells, within the intestines of individuals suffering from inflammatory bowel disease, and that pro-inflammatory lymphocytes further attract myeloid cells, including MDSC, into localized inflamed intestinal tissues. Currently, it has been found that MDSC infiltration is observed in animal models of CAC and patients with CAC, and these myeloid cells play a crucial role in facilitating the progression of IBD to CAC. Moreover, investigating the regulatory network of MDSCs in the development of CAC can help to explore new anti-tumor immunotherapeutic regimens for CAC targeting MDSCs, and enhancing the therapeutic effect of anti-CAC treatment is a novel approach. However, the mechanisms by which MDSCs control the progression of IBD to CAC remain largely unexplored. Here, we summaries the potential mechanisms of action of MDSC in the development of CAC from inflammatory bowel disease at this stage with other roles such as immune pathways, cytokines, and intestinal flora, and the potential ways in which MDSCs impact intestinal epithelial cells, resulting in the formation of low-grade and high-grade allopatric hyperplasia, and summarize the current therapeutic strategies for targeting MDSC-related CAC, as well as the research stages, existing findings and potential shortcomings of related studies, provide new insights into the specific ways in which MDSCs contribute to the progression of inflammatory bowel disease (IBD) to colorectal cancer (CAC) in the future, and also help to further explore the potential targets related to MDSCs for future CAC treatments, opening up new possibilities for effective therapeutic interventions.

MDSC and colitis-associated colorectal cancer

MDSCs play a critical role in the progression of colorectal cancer associated with colitis. MDSC are a diverse group of immature cells that originate from myeloid cells. In the bone marrow, hematopoietic progenitor cells normally undergo a process of differentiation into myeloid progenitor cells. These myeloid progenitor cells then continue to differentiate into granulocyte–macrophage precursors, followed by further differentiation into monocyte/dendritic cell precursors as well as mature myeloid cells, etc., and travel to secondary lymphoid organs, where they undergo further differentiation into monocytes and neutrophils to carry out specialized tasks (Wu et al. 2022b ). MDSC can be broadly classified into polymorphonuclear cells (PMN-MDSC) and monocytes (M-MDSC), which share phenotypic and morphological similarities with neutrophils and monocytes, respectively. The myeloid cell markers CD33 + , CD11b + , HLA-DR low/- and Lin- can be used to identify human MDSC, which are divided into two main subgroups, the M-MDSC (CD33 + CD11b + CD14 + CD15- HLA-DR low) subgroup and the PMN-MDSC (CD33 + CD11b + CD14- CD15 + HLA-DR-) subpopulation (Gabrilovich 2017 ). However, under conditions of chronic inflammation or tumor, due to the abnormal proliferation of bone marrow, a variety of pro-inflammatory factors may be produced, interfering with the normal maturation of myeloid cells and lead to an increase in the number of immature myeloid cells, and these heterogeneous populations, which have similar physical characteristics to monocytes or granulocytes but whose specific surface molecular signatures differ from those of monocytes or granulocytes are collectively referred to as MDSCs, whose main feature is the potent immune-suppressing function under pathological conditions such as inflammation or tumor. MDSCs can exert immunosuppressive effects through a variety of pathways and mechanisms, including the upregulation of nitric oxide synthase (iNOS), arginase-1 (ARG-1), reactive oxygen species (ROS), etc. to inhibit lymphocytes, and indirectly inhibiting the body’s immune response by suppressing regulatory T cells (Tregs). Recent research has shown a strong link between MDSC expression and the advancement of CAC during the transition from colitis to CAC using clinical specimens and mouse animal models, and MDSCs have become a key target in current oncology research due to their abundant presence in the tumor microenvironment (Chen et al. 2022a , b ). MDSC infiltration is often seen at sites of inflammation in patients with chronic inflammatory diseases and tumor, relevant studies have shown that certain pro-inflammatory mediators present in the microenvironment, like IL-6, contribute to the promotion of MDSC accumulation in the pathological state of chronic inflammation and malignancy (Laws et al. 2023 ). Prostaglandin E2 (PGE2) is a significant lipid mediator that produced in the sustained inflammatory response, also further recruits MDSC, which in chronic inflammation further produce S100A8/A9 proteins, these calcium-binding proteins are mainly secreted by neutrophils and activated monocytes, and interestingly, these proteins in the inflammatory microenvironment in the inflammatory microenvironment further recruiting more MDSC causing further MDSC accumulation, the inflammatory microenvironment can also further increase the generation of reactive oxygen species (ROS) and pro-angiogenic factors like vascular endothelial growth factor (VEGF), which can contribute to MDSC accumulation and immunosuppressive activity, in addition, the tumor microenvironment and persistent inflammatory stimuli can also lead to an additional boost in the production of tumor necrosis factor alpha (TNF-α) by tumor cells, which also further recruits MDSC and enhances MDSC-associated immunosuppressive activity (Wang et al. 2023a , b , c , d , e ). Multiple research studies have consistently shown that reducing MDSCs can effectively slow down tumor progression and lead to anti-tumor effects, and all these evidences suggest that MDSCs play unique and crucial roles in the progression of inflammatory bowel disease to CAC development (Krishnamoorthy et al. 2021 ; Liao et al. 2019 ; Wang et al. 2023e ).

Potential role of MDSC

The primary feature of MDSC is their capacity to inhibit the immune response. Each subtype of MDSC has different characteristics that affect their ability to regulate various components of the immune response. For example, PMN-MDSC primarily utilizes prostaglandin E2 (PGE2), arginase 1 and ROS for immunosuppression, whereas M-MDSC utilizes NO, IL-10, TGF-β, and PD-L1 for the same purpose (Youn et al. 2008 ).

Immune pathway related

Stat3 pathway.

In the tumor microenvironment, MDSC are activated by multiple mechanisms. The crucial role is played by the transcription factor STAT3 (Zhang et al. 2024 ). STAT3 is an important oncogenic transcription factor, and phosphorylation of Bcl-2 regulates the expression of genes that prevent apoptosis, angiogenesis-related (e.g., VEGF), and some specific factors (e.g., S100A9), which are all relevant to colitis-associated colorectal cancer tumor invasion, metastasis and prognosis.

MDSC express receptors for S100A8 and S100A9, Wang et al. ( 2020 ) used CRISPR CAS9 to knock down specific immunosuppressive factors in a mouse tumor model and found that STAT3 has a significant role in controlling the differentiation of MDSCs. Additionally, STAT3 was found to increase the expression of S100A8/9 proteins, which promotes the clustering of MDSCs in the tumor microenvironment (TME). This clustering ultimately leads to the activation and multiplication of MDSCs (Wang et al. 2023a , b , c , d , e ). Using single-cell cytokine profiling for immunoassay, G. Qin et al. found that MDSCs were significantly expressed in both clinical samples and mouse models of tumors, Additionally, they observed that specific factors like GM-CSF, G-CSF, IL-6, VEGF, etc., amplified the quantity of MDSCs in the tumor microenvironment and impeded their subsequent differentiation, and these factors further activated the JAK/STAT signaling pathway to stimulate myeloid cell production and promote MDSC expansion (Qin et al. 2023 ). Wu et al. found that the only inhibitory receptor in the Fcγ receptor family, FcγRIIB, was expressed in tumor-infiltrating MDSCs by constructing a mouse model of Fc γ receptor IIB receptor deficiency in the Immunoglobulin (Ig) Fc region, and using adoptive cell transfer, mRNA sequencing, and flow cytometry analysis to discover that the only inhibitory receptor in the Fcγ receptor family was responsible for upregulating the presence of tumor-infiltrating MDSCs. This receptor also played a role in promoting the production of MDSCs by hematopoietic progenitor cells through the Stat3 signaling pathway, thereby bolstering the immunosuppressive functions of MDSCs, resulting in tumor immune evasion (Wu et al. 2022a , b ). In addition, there are also studies on multifunctional spore shell nanoparticles (CN) encapsulated on the surface of probiotics, and it was found that CN-encapsulated probiotics were successful in treating inflammatory bowel disease and preventing colorectal cancer. The activation of STAT3 was discovered to enhance the functionality of epithelial cells and prevent the death of intestinal epithelial cells (IECs). This, in turn, has an impact on the integrity of the intestinal barrier and the stability of the intestinal microenvironment, and that blocking the IL-6 -STAT3 signaling pathway through CN-encapsulated probiotics could prevent colitis-related colorectal cancer. Blocking the IL-6-STAT3 signaling pathway by CN-encapsulated probiotics prevented the prevention of colitis-associated colorectal cancer was achieved, which further confirmed the pathogenic role of STAT3 in colitis-associated colorectal cancer (Song et al. 2021 ).

NF-κB pathway

Studies have confirmed that IL-1β activates the NF-κB signaling pathway to promote MDSCs mobilization and proliferation. As a result, the progression of gastritis and gastric cancer is promoted (Tu et al. 2008 ). One of the functions of activating the NF-κB signaling pathway is to promote the mobilization of MDSCs, which in turn promotes colorectal tumorigenesis associated with ulcerative colitis. This study found that the glycoheterotrophic enzyme fructose 1,6-bisphosphatase 1 (FBP1) can directly interact with IκBα, thereby inhibiting NF-κB activation and suppressing colorectal tumorigenesis (Zhu et al. 2023 ). When VEGF is highly expressed in the TME, it can activate the NF-κB pathway. As a result of this activation, FLT3L expression is suppressed, which promotes the abnormal differentiation of myeloid progenitor cells into MDSCs. The NF-κB family member c-Rel was utilized in the study, and the use of a small-molecule inhibitor targeting c-Rel led to a notable reduction in the tumor-suppressing capabilities of MDSCs, ultimately resulting in the inhibition of tumor growth (Li et al. 2020 ). The regulation of apoptosis and proliferation of IECs is significantly influenced by the NF-κB signaling pathway. Furthermore, it plays a crucial role in preserving the integrity of the intestinal barrier and promoting the body’s defense against pathogens. The NF-κB signaling pathway is essential for preserving the integrity of the intestinal epithelium and ensuring a balanced immune response in the intestine, when NF-κB is deficient, this leads to the death of IECs, decreased production of antimicrobial peptides, and the movement of bacteria into the mucosa. These events contribute to the progression of inflammatory bowel cancer (Nenci et al. 2007 ). The close relationship between the NF-κB signaling pathway and the formation, recruitment, and activity of MDSCs is clearly evident, and is a key factor in colitis-associated colorectal cancer.

COX-2/PGE2 pathway

MDSC inhibitory activity is significantly influenced by prostaglandins, particularly prostaglandin E2 (PGE2). PGE2 has been linked to the promotion of tumor growth, angiogenesis, and the suppression of the immune system.

Relevant studies have shown that PGE2 inhibits immunity by recruiting MDSC, among others, and contributes to tumor angiogenesis. In the early stage of tumor formation, COX-2/PGE2 pathway can drive the inflammatory microenvironment, which promotes downstream signaling, and consequently, tumor progression in an irreversible direction. In the in vivo environment of colitis-associated colorectal cancer patients, overexpression of COX-2 leads to increased production of vascular growth factors. This, in turn, stimulates endothelial cell migration, leading to increased invasiveness and the spread of cancer cells is inhibited by reducing the Bcl-2 gene expression and decreasing apoptosis, etc. Thus, increased expression of COX-2 is associated with the development, progression and spread of colorectal cancer. CXCL1 is a pro-angiogenic CXCL1 is a pro-angiogenic chemokine, and PGE2 can induce CXCL1 expression, increase MDSC infiltration in colitis-associated colorectal cancer mouse models, promote in vivo tumor growth and increase tumor micro vessel formation. In recent years, there is strong evidence indicating that the prolonged use of NSAIDs significantly decreases the likelihood of developing colorectal cancer (CRC) (Friis et al. 2015 ), an interesting finding that has led to a reexamination of the "miracle drug" aspirin and its target cyclooxygenase (COX), COX is responsible for metabolizing arachidonic acid, released from membrane phospholipids by phospholipase A2 (PLA2), into prostaglandin H2 (PGH2) via specific isoenzymes and TXX2, an enzyme that is a key component of prostaglandin H2 (PGH2). COX has the ability to break down arachidonic acid, which is released from membrane phospholipids by phospholipase A2 (PLA2), and convert it into prostaglandin H2 (PGH2), which is converted by specific isomerase and TXA synthase into different prostaglandins (such as PGE2, PGD2, PGF2α, PGI2, etc.) and TXA2, and COX has two isozymes, COX1 and COX2, with COX-1 being responsible for the maintenance of tissue homeostasis during regular physiological conditions. COX-1 is primarily responsible for maintaining the necessary basal levels of prostaglandins for tissue balance in normal physiological situations. On the other hand, COX-2 is predominantly found in inflammatory cells (Ye et al. 2020 ). Recent research has demonstrated that elevated levels of COX-2 in colon tumor cells lead to an increased production of PGH2 from arachidonic acid, and therefore the expression of PGE2 is increased in the microenvironment. PGE2 can enhance the progression of colorectal cancer by inducing apoptosis, promoting cell proliferation, stimulating angiogenesis, and facilitating metastasis (Karpisheh et al. 2019 ). The expression of MYO10 (signaling axis) is decreased when COX-2 is knocked down, resulting in a decrease in the migratory and invasive capabilities of colon cancer tumor cells (Liu et al. 2023a , b ). At the same time, COX-2 inhibitors, resist epithelial-mesenchymal transition (EMT) alterations in CRC cell lines (This is also a key process in cancer cell metastasis, where epithelial cells undergo a transformation from their original characteristics to mesenchymal characteristics, resulting in enhanced motility and migratory abilities). It has also been shown that co-targeting COX2 with BRAF + EGFR durably inhibits tumor growth capacity in patient-derived tumor xenograft models (Ruiz-Saenz et al. 2023 ). COX2 inhibition represents a strategy that could overcome associated CRC treatment resistance. Currently, a variety of COX-2 small molecule inhibitors, such as rofecoxib, have entered the phase III clinic (NCT00031863), and other small molecule inhibitors, such as imrecoxib (ChiCTR2100051644), lumiracoxib (NCT00170898), and meloxicam (NCT01886872), have also entered the clinic and have been successfully marketed. NCT01886872) have also entered the clinic and been successfully marketed. It is believed that with the development of COX-2 inhibitors with higher safety and specificity in the future, it will be more helpful in preventing and controlling the development of tumors.

Cytokine-related

New research has indicated that specific inflammatory cytokines and chemokines have the ability to promote the proliferation of MDSCs. On one side, they are withholding crucial amino acids from T cells, induce oxidative stress, and are involved in regulating the functions of Tregs and T helper (Th)17 cells, etc. On the other hand, They stimulate MDSCs to release reactive oxygen radicals (ROS), inducible nitric oxide synthase (iNOS), and arginase 1 (Arg-1), which suppress T cell activity and promote T cell apoptosis (Ma et al. 2020 ). At the same time, an imbalance between pro-inflammatory and anti-inflammatory cytokines can fuel the progression of IBD, leading to the development of colitis-associated colorectal cancer.

IL-10: A cytokine known to suppress inflammation. Chronic inflammation in ulcerative colitis contributes to the accumulation of high levels of MDSCs in the colon, and in turn, high levels of MDSCs produce higher levels of IL-10, but the function of IL-10 is altered in this environment IL-10 instead activates STAT3, which results in increased expression of two genes, DNMT1 and DNMT3b-, contributing to the silencing of a tumor suppressor (Ibrahim et al. 2018 ). Leading to possible IEC heteroplasia, driving a higher incidence of inflammatory bowel cancer.

IL-6: The progression of colitis-associated colorectal cancer was also significantly affected by IL-6. It has the ability to activate Janus kinase (JAK) and induce phosphorylation of the transcription factor STAT3 downstream, thus promoting the development of cancer. Apart from the JAK/STAT3 pathway, IL-6 can also exacerbate the intestinal inflammatory response in patients and promote colitis-associated colorectal cancer by regulation of Th17 and Treg cell proliferation and function (Wang et al. 2023a , b , c , d , e ). IL-6 produced by MDSCs can hinder the development and activity of CD4( +) T cells, ultimately promoting tumor development (Tsukamoto et al. 2013 ). It has been shown that blocking IL-6 enhances the therapeutic effect of immunotherapy by inducing and recruiting higher levels of CD4 + /CD8 + effector T cell production and recruitment in the tumor microenvironment (Hailemichael et al. 2022 ).

TGF-β: It can exert its anticancer effects through antiproliferative, pro-apoptotic and inhibiting the production of inflammatory factors with pro-tumor activity. However, TGF-β also modulates the immunosuppressive effects of MDSCs and hinders the ability of immune cells to fight against tumors, and also promotes epithelial-mesenchymal transition (EMT), thus accelerating tumor metastasis (Yang et al. 2008 ).

TNF-a: It is an important pro-inflammatory factor that plays a crucial role in the advancement and growth of colorectal cancer linked to colitis. This inflammatory mechanism is primarily responsible for the sustained activation of the NF-κB signaling pathway. It can also stimulate colitis-associated colorectal cancer production by damaging DNA. It has been shown that TNF-α indirectly mediates the effects of ROS on the stem cell microenvironment and plays an important role in epithelial cells (Hsu et al. 2022 ). Blocking TNF can attenuate colorectal cancer by altering the composition and activity of the microbiota (Yang et al. 2020 ).

Other relevant avenues

In the corresponding environment, MDSC release reactive oxygen radicals (ROS), etc. The large amount of reactive oxygen ROS etc. produced can cause oxidative stress. Studies have shown that oxidative stress induces the misvocalization of nuclear RNA or the DNA-binding protein TDP-43, which further leads to the overaccumulation of the R-loop (i.e., DNA in the form of an RNA heterozygous strand structure formed by DNA and RNA), which in turn triggers DNA damage and instability in the genome, resulting in the over-activation of the key enzyme for DNA repair, PARP1. This further trigger depletion of the coenzyme NAD + and ATP deficiency, which promotes the onset of mitochondria-dependent necrotic apoptosis in intestinal epithelial cells, which in turn drives the onset of spontaneous intestinal inflammation (Yang et al. 2024 ). In addition, excess ROS can cause damage to intestinal cells. This damage occurs through a variety of mechanisms, including induction of DNA mutations, impairment of protein function, alteration of epithelial permeability, and disruption of the intestinal epithelial barrier. These effects ultimately lead to cancer development and the proliferation of tumor cells (Wang et al. 2023a , b , c , d , e ; Zhang et al. 2023 ). Thus, inflammation-induced oxidative stress is clearly also important in the progression of colitis-associated colorectal cancer.

Several studies have shown that the metabolism of intestinal flora can also be closely related to the progression of colitis-associated colorectal cancer. Associated microbiota can induce aggregation of MDSCs and pro-inflammatory functions. For example, the presence of commensal Gram-negative gut bacteria can lead to the accumulation of MDSCs (Zhang et al. 2020 ). FadA adhesin produced by Clostridium nucleatum binds to E-calmodulin to produce pro-inflammatory factors and activates the TLR4/NF-κB signaling path way via lipopolysaccharide LPS to promote the recruitment of MDSCs to the infection site, this recruitment activates the Wnt/β-catenin signaling pathway, which further modulates the bacterial adhesion and invasion into the epithelial cells, ultimately, this process promotes the proliferation of colorectal cancer cells that Promotion of colorectal cancer (Yang et al. 2024 ). Enterotoxin-producing bacterium Enterobacteriaceae fragilis (ETBF) induces Th17 recruitment and inhibits T-cell proliferation, contributing to a pro-inflammatory milieu that favors the production and differentiation of pre-tumorigenic monocyte-derived cells (MDSCs), as a consequence, the chances of developing colorectal cancer may increase (Thiele Orberg et al. 2017 ).

Figure  1 illustrates the involvement of MDSC in colitis-associated colorectal cancer. However, the specific role of MDSC in the advancement of colitis-associated colorectal cancer is not fully understood, and more research is necessary to gain a comprehensive understanding of how MDSC contributes to the formation of inflammatory bowel cancer.

MDSC exerts relevant immunosuppressive functions in the inflammatory microenvironment and influences tumor development. Associated chemokines (CSF, VEGF, CXCLx, etc.) recruit MDSC in the inflammatory microenvironment; MDSC play an immunosuppressive role by inhibiting T-cell-associated functions; MDSC promote the involvement of Treg cells in the associated suppression of anti-tumor immune responses; MDSC promote epithelial-mesenchymal transition (EMT), tumor angiogenesis, and enhancement of tumor cell stemness

Effects of MDSC on intestinal epithelial cells

A sustained and persistent inflammatory response is critical to the development of colorectal cancer associated with colitis. This inflammatory response plays a role in every stage of tumor formation and development. Colitis significantly increases the risk of colorectal cancer due to the long-term accumulation of inflammation. Chronic inflammation and increased epithelial cell renewal will lead to the formation of both low-grade and high-grade heterogeneous proliferation of intestinal epithelial cells, which may promote the further transformation of IBD to colitis-associated colorectal cancer, and the poor prognosis of patients who progress from inflammatory bowel disease to colorectal cancer has a higher mortality rate. Recent studies have shown that patients with inflammatory bowel disease, MDSC promotes the proliferation of intestinal epithelial cells, triggers heteroplasia of intestinal epithelial cells, and enhances the ability of tumor cells to maintain stem cell properties, thus contributing to irreversible cancerous transformation of intestinal epithelial cells.

Promotes proliferation of IECs

Relevant studies have shown that under conditions of inflammatory bowel disease, MDSC-derived IL-6 activates STAT3 and stimulates intestinal epithelial cell IEC hyperproliferation. Invasiveness, proliferation and stemness of human colon cancer cells are closely related to Stat3 activation (Liu et al. 2022 ). Furthermore, it has been shown that elimination of Stat3 from epithelial cells greatly hinders the development of colitis-associated colorectal cancer. This is achieved by inhibiting cell proliferation and promoting apoptosis. It has also been shown that removal of cyclic guanosine-adenylate synthase (cGAS) promotes the recruitment and activation of MDSCs in the colon, and proliferation of intestinal epithelial cells and increased permeability of the intestinal barrier. It may promote colonic inflammation and cancer development (Hu et al. 2021 ).

Elicits heterogeneous hyperplasia in the IECs

MDSC promote tumor progression by promoting chronic inflammation, facilitating angiogenesis, and establishing a tumor microenvironment that suppresses the immune system. It has been shown that PAR2 deficiency in MDSC directly enhances its immunosuppressive activity by promoting STAT3-mediated reactive oxygen species production, contributing to IEC heteroproliferation (Ke et al. 2020 ). In the microenvironment of inflammatory bowel disease, MDSC-derived ROS may cause IEC heteroproliferation by damaging IEC DNA, inducing DNA alterations in epithelial cells and inhibiting their repair. Under inflammatory conditions, IL-6-activated STAT3 also plays an important role in IEC heteroplasia (Grivennikov et al. 2009 ).

Enhancement of tumor cell stemness

In an inflammatory environment, MDSCs can also promote colitis-associated colorectal cancer progression by promoting stemness in colon cancer cells. There have been reports indicating that netrin-1 inhibits PMN-MDSC recruitment and promotes tumor cell stemness, which is closely related to drug resistance and cancer recurrence after chemotherapy or immunotherapy (Ducarouge et al. 2023 ). MDSC-associated PGE2 induces iPSC in mice to acquire the characteristics of cancer stem cells (CSCs) through the PI3K/Akt axis. Thus, it is clear that PGE2 also contributes to the promotion of cancer cell stemness that leads to colitis-associated colorectal cancer (Minematsu et al. 2022 ).

Targeting MDSC-related therapeutic strategies

MDSCs consistently recruited in the tumor microenvironment have the ability to inhibit T cell proliferation and impair their function. Therefore, targeting MDSCs is a potential strategy for corresponding tumor therapy. We summarized the current relevant studies targeting MDSC therapy as shown in Table  1 .

Depletes MDSC

Chemotherapeutic agents such as gemcitabine, cyclic tetrazolamide (CTX), 5-fluorouracil, oxaliplatin, paclitaxel, and others have the potential to improve the effectiveness of immunotherapy in combating tumors. They achieve this by targeting the immunosuppressive cells present in the tumor microenvironment (Gürlevik et al. 2016 ). Gemcitabine, as a nucleoside analogue, is now widely used in the first-line treatment of various solid tumors. Gemcitabine can directly kill tumor cells and down reduce the quantity and activity of MDSCs, and enhance T-cell-mediated immune responses against tumors (Jiang et al. 2023 ). Relevant studies have shown that chemotherapeutic agents such as CTX can decrease the expression level of multidrug and toxin extrusion transporter protein ABCB1 in Treg cells, thereby depleting Treg and MDSC cells and inhibiting tumor growth (Galluzzi et al. 2020 ). Chemotherapeutic drugs such as fluorouracil (5-FU) has been recognized as an important drug for colorectal cancer since it was applied in the clinic in 1957. It can block the synthesis of the nucleoside thymidine, which is essential for DNA replication, and the relevant studies have found that MDSCs in several mouse tumor models display lower levels of the enzyme targeted by 5-FU. Moreover, cells with reduced thymidine synthase expression are highly vulnerable to cell death caused by 5-FU. Due to their low expression of thymidylate synthase, cells are highly susceptible to cell death induced by 5-FU. As a result, 5-FU can selectively eliminate MDSCs by inducing apoptosis, and the administration of 5-FU treatment significantly reduces tumor growth rate in mouse models and achieves therapeutic efficacy (Kim et al. 2021 ). A randomized controlled trial conducted on colon cancer patients in phase II has also shown that 5FU can effectively combat tumors by specifically targeting and eliminating MDSC. Studies conducted both in vitro and in vivo have demonstrated that 5FU has strong cytotoxicity against MDSC, thereby mediating immune tolerance. The 5FU-induced reduction in MDSC also further enhances the production of interferon-Y (IFN-Y) by CD8( +) T cells, which contributes to the promotion of anti-tumor responses (Wang et al. 2023a , b , c , d , e ).

Tyrosine kinase inhibitors such as sunitinib can directly target the amplification signaling pathway of MDSC to achieve the purpose of MDSC depletion. Sunitinib is a targeted therapeutic drug that can inhibit the activity of multiple receptor tyrosine kinases, and relevant clinical studies have demonstrated that sunitinib inhibits the growth and spread of cancer cells, showing obvious anti-tumor activity and safety (Heinrich et al. 2024 ; Vallilas et al. 2021 ). A recent study reported that two multi-targeted tyrosine kinase inhibitors, carbonatitic or celecoxib, were able to reduce MDSC levels in a mouse model of squamous cell carcinoma (Huang et al. 2020 ). Additionally, there are studies that have assessed the effectiveness and safety of combining the tyrosine kinase inhibitor cabozantinib with a PD-L1 inhibitor in patients with advanced colorectal cancer within a specific colorectal cancer population. The results demonstrated that this treatment plan demonstrated significant effectiveness in fighting tumors and had tolerable side effects in patients with advanced colorectal cancer that did not respond to previous treatments (Saeed et al. 2024 ).

Depleting MDSCs continues to be a crucial approach for enhancing anti-tumor immunity, and as we uncover more MDSC-related targets, this strategy will become even more significant in the future, it will also surely provide new ideas for future therapeutic options.

Induces MDSC differentiation

To successfully decrease the quantity of MDSC in mouse tumor models and cancer patients, we can also achieve our desired effect by inducing differentiation of immature myeloid cells.

Vitamins such as vitamin D and vitamin E can induce MDSC cell differentiation. Relevant studies have shown that 1,25-dihydroxyvitamin D3 plays a crucial role in controlling cell growth and differentiation, and recent research has demonstrated that 1,25-dihydroxyvitamin D3 has the ability to transform normal human myeloid cells into macrophages and monocytes, vitamin E enhances the immune response by reducing the production of ROS and NO and reducing the presence of immature MDSCs (O'Mahony et al. 2023 ). ATRA, also known as trans-retinoic acid, is a substance derived from vitamin A. It is used in the production of myeloid cells. The transformation of MDSCs into fully developed myeloid cells is significantly influenced by it. Several studies have demonstrated the ability of ATRA to activate the ERK1/2 kinase pathway and then upregulate glutathione, further scavenging ROS, resulting in a decrease in the amount of ROS in MDSCs. After administering ATRA treatment, the number of MDSCs in a mouse model of tumor showed a significant decrease, and MDSCs were stimulated to differentiate into dendritic cells and macrophages, which transformed their immune-suppressing function into an immunogenic one, greatly improving the anti-tumor effect. greatly improved the anti-tumor effect (Hengesbach and Hoag 2004 ). 24 patients with advanced melanoma participated in a phase 2 clinical trial (NCT03200847) to evaluate the effectiveness of combining ATRA and Pembrolizumab, it was demonstrated that the ATRA combination therapy was well-tolerated and safe in patients with melanoma. Additionally, it effectively decreased the proportion of MDSCs while increasing the proportion of myeloid cells, and that the majority of the patients achieved significant symptomatic relief. In other words, patients treated with ATRA had reduced levels of MDSCs and promoted MDSC differentiation, and this alteration in differentiation subsequently decreased the immunosuppressive effects of MDSCs, translating into enhanced immunotherapy efficacy and safe and effective treatment (Olson and Luke 2023 ).

Also some researchers have found in mouse tumor models that diosgenin not only triggers apoptosis in MDSCs, but also promotes their transformation into M1-like macrophages while reducing the number of M-MDSC cells. This discovery weakens the development of colitis-associated colon cancer, making it a promising natural option for effectively preventing CAC. We believe that in the future, we can explore more relevant drugs for inducing MDSC differentiation, which can help the treatment of CAC (Xun et al. 2023 ).

Inhibits MDSC recruitment and migration

Under pathological conditions such as inflammation, tumors, etc., this leads to the recruitment and migration of MDSCs, which then exert their immunosuppressive function. Therefore, there are many studies to stop this process.

VEGF inhibitor vascular endothelial growth factor (VEGF) is a distinctive growth factor that specifically targets vascular endothelial cells, stimulating their growth and enhancing their functionality. It is essential for increasing vascular permeability, breaking down the extracellular matrix, supporting endothelial cell motility and growth, and stimulating the growth of fresh blood vessels via the process of angiogenesis. Tumor cells are the main source of VEGF and play a role in TME. VEGF, in turn, Stimulates the growth of fresh blood vessels (angiogenesis) and induces MDSCs to enter the tumor. To address this problem, monoclonal antibodies (mAb) have been developed that specifically target VEGF. These mAb have shown promising results in inhibiting tumor growth in mouse cancer models and in human patients (Shojaei et al. 2007 ). Preclinical and clinical studies have shown that bevacizumab, a recombinant human monoclonal antibody targeting vascular endothelial growth factor, is effective in reducing intratumorally MDSCs. During a Phase 2 clinical trial (NCT01730950), the utilization of bevacizumab demonstrated a significant improvement in progression-free survival (PFS) in patients with recurrent glioblastoma (GBM). This treatment effectively reduced the occurrence of MDSC and prevented MDSC recruitment and movement, resulting in clinically significant efficacy. A trial has shown a reduction in circulating MDSCs after combination therapy with bevacizumab and 5-fluorouracil, oxaliplatin in patients with colorectal cancer (Limagne et al. 2016a , b ). Perhaps we can explore more relevant combination therapy options in the near future.

HIF-1α inhibitor: The increased expression of HIF-1α in the hypoxic environment of TME can lead to an increase in glycolytic enzymes and lactate transporter proteins in MDSCs as a way to promote MDSC differentiation and proliferation. Moreover, HIF-1α can also improve mitochondrial respiratory function through the PI3K/AKT and JAK2/STAT3 pathways, as well as attenuate cellular oxidative stress and reduce ROS production, thus alleviating MDSC cell injury. Meanwhile, the acidic environment formed by hypoxia promotes MDSC proliferation and enhances the inhibitory function of MDSC on T cells through HIF-1α. HIF1α hypoxia-inducible factor. Stable expression under hypoxic conditions. Plays an important role in MDSC accumulation. Therefore, MDSC recruitment in TME can be reduced by HIF1α inhibitors. Against hypoxia, a phase 1/2 clinical study (NCT01522872) involving TH-302 (efaproxiral), a hypoxia-activated prodrug, demonstrated extended survival in patients with myeloma, combating the effects of hypoxia (Laubach et al. 2019 ). In mouse kidney and mammary tumor models, researchers found that a nano-enzyme called Zr-CeO exhibited promising results in diminishing the recruitment of MDSCs, thereby enhancing the efficacy of PD-1 inhibitors in combating tumors (Mo et al. 2023 ).

Calcium-binding proteins S100A8 and S100A9, it plays a crucial role in the accumulation of MDSCs. Reduction of S100A8/A9 reduces MDSC accumulation in several mouse tumor models. Tiquinamide has been proven to decrease the infiltration and accumulation of MDSCs, with S100A9 as one of the targets. In a phase II clinical trial, results showed that administration of guanosine amide was effective in reducing the infiltration and accumulation of MDSCs, of which S100A9 is a specific target. This significantly slowed disease progression in metastatic CRPC. Furthermore, tiquinamide demonstrated enhanced progression-free survival rates in metastatic refractory prostate cancer (mCRPC) (Mehta and Armstrong 2016 ). The results indicate that the aggregation of MDSCs is significantly influenced by the involvement of S100A8/A9.

Chemokine receptors, it plays a key role in directing MDSCs to the tumor site. MDSCs are mainly identified by their expression of the chemokine receptor CCR2 and are attracted to tumors that produce the chemokines CCL2 and CCL5.

Research has demonstrated that blocking the CCL2/CCR2 axis with CCR2 antagonists, either on their own or in conjunction with other compounds, reduces the presence of MDSC in tumors and enhances tumor condition in preclinical mouse models (Nywening et al. 2016 ). The CCR2 inhibitor PF-04136309 has been shown to improve the survival of pancreatic cancer patients in combination with FOLFIRINOX, improves the chances of survival for individuals diagnosed with pancreatic cancer, demonstrating an improved antitumor response (NCT01413022).

In conclusion, these studies suggest that drugs targeting CCR2 can effectively decrease the levels of MDSCs and enhance the survival rates of individuals with cancer.

The small molecule inhibitor SX-682, which targets CXCR1 and CXCR2, effectively reduces the accumulation of tumor-infiltrating myeloid-derived suppressor cells (MDSCs). When used with checkpoint inhibitors, it has demonstrated promising results in improving the body’s immune response against tumors.

Studies have indicated that the combination of the CCR5 antagonist maraviroc with pembrolizumab is feasible in metastatic colorectal cancer (NCT03274804) (Liu et al. 2023a , b ). It is uncertain whether these drugs will be successful in reducing the buildup of MDSCs and enhancing the immune system’s ability to fight tumors.

Targeting CSF1-R is also a method to block the migration of MDSCs to tumor locations. Binding of CSF1-R to ligand CSF1 stimulates the development and proliferation of myeloid cells. Studies performed in a mouse tumor model demonstrated that blockade of CSF-1R using a specific inhibitor (BLZ945) reduced the aggregation of MDSCs and shrunk tumor size. This highlights the important role of CSF-1R signaling in recruiting MDSCs (Mao et al. 2016 ).

Currently, a phase I clinical trial (NCT02880371) is in progress to assess the safety and initial effectiveness of the colony-stimulating factor-1 receptor-specific inhibitor ARRY-382 (PF-07265804). The trial is designed to evaluate the combination of ARRY-382 and pembrolizumab in advanced solid tumors (Johnson et al. 2022 ).

Modulates the immunosuppressive function of MDSC

Modulating the immunosuppressive function of MDSCs has been utilized as a therapeutic approach to enhance the function of T cells and other immune cells.

The researchers found that by blocking iron death in the mouse model, they were able to eliminate the inhibitory effect of PMN-MDSC on T cells. This led to a significant reduction in tumor growth. Moreover, the use of iron death inhibitors combined with anti-PD1 treatment exerted a better anti-tumor therapeutic effect (Kim et al. 2022 ). M-MDSC induced an increase in adenosine levels, and depletion of adenosine in TME using polyethylene glycolate adenosine deaminase (PEG-ADA) improved the efficacy of immune checkpoint inhibitor (ICI) therapy in mouse studies. This provides new ideas for overcoming resistance to ICI in cancer patients (Sarkar et al. 2023 ).

As previously described, the accumulation and growth of MDSCs in tumors are greatly affected by the STAT family of transcription factors. Among these factors, STAT3 plays a particularly vital role in this mechanism.

The STAT3 inhibitor Napabucasin significantly increased the survival of melanoma-bearing mice. This inhibitor successfully eliminated the immunosuppressive capacity of mouse MDSC and human M-MDSC (Bitsch et al. 2022 ). Experimental evidence from a Phase 1b clinical trial (NCT01563302) demonstrated that the STAT3 inhibitor AZD9150 (Danvatirsen) was able to reduce granulocyte MDSC levels. In addition, Danvatirsen alone or in combination with checkpoint inhibitors has shown encouraging results in reducing PMN-MDSC levels in diffuse B-cell lymphoma. Combination therapy with Danvatirsen and the anti-PDL1 monoclonal antibody Durvalumab has demonstrated encouraging outcomes in the treatment of individuals suffering from advanced solid tumors (NCT02983578) (Reilley et al. 2018 ).

TGF-β promotes MDSC expansion, differentiation and immunosuppressive functions. A TGF-βR inhibitor, LY3200882, was used in a clinical study (NCT02937272) in patients with solid tumors (Yap et al. 2021 ). As an anti-cancer therapy, inhibition of TGF-β signaling may affect cardiac development and function, which may be a major challenge in the study. Therefore, the development of anti-TGF-β combination therapy is necessary to enhance the clinical efficacy and reduce the toxicity.

Toll-like receptor (TLR) 7/8 small molecule agonists are strong activators of mature activation in antigen-presenting cells (APCs). In a study using mice to model colon cancer, the TLR 7/8 agonist resiquimod (R848) has been shown to reduce MDSCs within tumors and in the bloodstream. In addition, it inhibits the immunosuppressive capacity of MDSCs. In another study, the combination of oxaliplatin and the Toll-like receptor agonist R848 was found to disrupt the differentiation of myeloid-derived suppressor cells (MDSCs), thereby enhancing oxaliplatin resistance in colorectal cancer patients. In addition, this combination therapy improves the anti-tumor efficacy of oxaliplatin (Liu et al. 2020 ).

Histone deacetylase (HDAC) inhibitors are able to impair MDSC-mediated immunosuppression. It has been shown that when the HDAC 6 inhibitor ricolinostat was used in combination with the HDAC 1 inhibitor Entinostat, both MDSC populations were completely eliminated and tumor progression was delayed in several tumor models (Luke et al. 2023 ). However, the HDAC 6 inhibitor ricolinostat alone only reduced M-MDSC, failed to reduce PMN-MDSC and did not reduce tumor growth in the tumor models. A similar situation occurred with Entinostat alone (Hashimoto et al. 2020 ). Valproic acid is an HDAC inhibitor that has been shown to reduce tumor infiltration by MDSCs, attenuate immunosuppression caused by MDSCs, and improve the efficacy of anti-PDL1 immunotherapy (Xie et al. 2020 ). Entinostat combination therapy has shown positive response in advanced solid tumors, as reported in a phase 2 clinical trial (NCT 01928576).

In a clinical trial (NCT02903914), it was demonstrated that small molecule inhibitors of ARG-1 can reduce levels of iNOS and COX-2, while also regulating immunosuppressive functions in MDSC. The drug combination, which included the ARG-1 inhibitor CB-1158, was proven to be both safe and effective in patients with advanced and metastatic tumors.

A recent clinical study (NCT03043313) conducted on individuals diagnosed with colorectal cancer has shown promising results in the use of tucatinib, a small molecule HER2 inhibitor, in combination with trastuzumab. and was well tolerated by the participants (Strickler et al. 2023 ).

During a phase 3 clinical trial (NCT00843635), tadalafil treatment was found to be well tolerated and had a positive impact on patients with HNSCC by lowering MDSC levels. It was observed that higher doses of tadalafil had no relevant immunomodulatory activity, but intermediate doses were found to be most effective.

Targeted therapy related to colorectal cancer

Current targeted therapies for colorectal cancer.

Looking at Table  2 , we can see that although some phase I and small phase II clinical trials have achieved interesting results, phase II and III trials in a wider patient population have not shown convincing efficacy, and the treatment also faces the challenge of drug-related toxicity and the emergence of drug resistance. In the meantime, researchers are exploring combinations of drugs, exploring new targets, and refining targeted therapies in the future to enhance the quality of life and prognosis for patients with bowel cancer.

Challenges, opportunities, and possible future directions

It is undeniable that additional research is necessary in the future to tackle the challenges related to the development of MDSC in inflammatory bowel disease and its link to the initiation of colorectal cancer:

1. Firstly, the definition of MDSC itself is indeterminate, which hinders our study of it (Hegde et al. 2021 ). The definition of extant DSCs is inherently limiting. Only two categories are currently defined, M-MDSC and G-MDSC. but there are no known major regulators of MDSC properties and a range of MDSC-like phenotypes are observed in inflammation or cancer. Therefore, we need to develop a new research idea to refine the definition of MDSC.

2. Can MDSC be used as a biomarker of true response? It is not yet known and clinical trials are still needed to shed light on the issue. Due to the relatively small number of colitis-associated colorectal cancer-related cases, many aspects are not yet clear. As its incidence increases significantly, we should carry out large-scale, multicenter population-based cohort studies to formulate effective proactive preventive measures to reduce the occurrence of tumors; meanwhile, further studies on its cancerous mechanism will help to clarify the process of tumor development and progression, laying a theoretical basis for clinical early prevention as well as the selection of therapeutic targets.

3. Some other issues we need to be aware of:

Targeting molecules related to MDSCs can lead to anti-tumor effects that may be difficult to replicate in a clinical environment. Firstly, equivalence from mouse tumor models to human MDSC therapies is a challenge. Second, might there be functional differences between human and murine cell subpopulations? Human neutrophils may possess a more potent ability to kill tumor cells. Long-term inhibition of all neutrophils may be undesirable.

The activity of MDSCs may vary depending on the type of cancer and the stage of the disease. The types of MDSCs that play a major role may also be different in different cancer types. For example, PMN-MDSCs are the predominant cell population undergoing proliferation in various forms of cancer. However, in tumors like melanoma, M-MDSCs play a more critical role. Secondly, the mechanisms by which MDSCs may mediate inhibitory activity may be different at different stages of cancer. Therefore, it is important to identify the population of patients targeting MDSCs and their choice of drugs for different targets.

Moreover, different subtypes of MDSC such as PMN-MDSCs and M-MDSCs have different differentiation pathways, and different mechanisms of activity inhibition. Targeting a specific group of cells alone may not be effective in managing tumor growth. It is important to explore the combined targeting of multiple therapy approaches to effectively control tumor progression. There is a subset of patients who actually have no increase in MDSCs. Therefore, MDSC targeting may not be effective in these patients.

Therefore, the selection of therapeutic targets is important. And finding combinations of different drugs for different biological pathways may be a more effective way to target MDSCs. However, the toxicity potential of the drugs used, and the appropriate maximum clinical dose are critical issues to consider. Therefore, when selecting patients to be enrolled in the study, we need to analyze the tumor in detail, such as focusing on the detailed tumor stage and so on. At the same time, we should provide rational and individualized drug treatment plans for different categories of CAC patients. Moreover, we should focus on the development of preclinical models to achieve the transition from preclinical to clinical, and explore the reasonable combinations of treatments that can be adopted in the clinic.

Colitis-associated colorectal cancer is a specific form of colorectal cancer that develops as a result of inflammatory bowel disease (IBD). MDSCs, short for myeloid-derived suppressor cells, are a group of immature myeloid cells with immunosuppressive abilities that are crucial in the progression of inflammation and cancer. There is increasing evidence suggesting that MDSCs play a crucial role in the progression of colitis-associated colorectal cancer. Under pathological states such as inflammatory microenvironment and tumor, multiple immune pathways such as STAT3, NF-κB, COX-2/PGE2 activate to recruit MDSCs to exert the corresponding immunosuppressive functions, while some inflammatory cytokines and chemokines can promote the proliferation of MDSCs and stimulate MDSCs to release reactive oxygen radicals (ROS), inducible nitric oxide synthase (iNOS), Arginase 1 (Arg-1), and other compounds, these compounds impede T cell activity and induce T cell apoptosis, and can cause intestinal cell damage by changing epithelial permeability, destroying the intestinal epithelial barrier, and mismanaging the intestinal flora, leading to heterogeneous hyperplasia of intestinal epithelial cells and proliferation of tumor cells, as well as prompting intestinal epithelial cells to undergo irreversible cancerous changes, and transforming from inflammatory bowel disease to colorectal cancer.

Immunotherapy has opened new avenues for the treatment of some malignancies. However, those used at this stage, e.g., Monoclonal antibodies targeting PD-1/PD-L1, have good efficacy only in some patients. Therefore, improving the effectiveness of immunotherapy is an important research direction. W what’s more, this paper summarizes the strategies for targeting MDSC-related therapies as well as the possible strategies for treating CAC against MDSC at this stage, to provide a reference for the discovery of more potential targets for MDSC in the future. Although there are many therapeutic targets for MDSC, the key questions of how to better improve T-cell efficacy and how to accurately combat MDSC remain to be revealed in future clinical studies. Perhaps in the near future, MDSC targeted therapy may open a new era of tumor immunotherapy.

Data availability

No data was used for the research described in the article.

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The current research was funded by the Research Project of the Jiangsu Commission of Health (Grant No.K2023062), and National Natural Science Foundation of China (82370533).

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Conceptualization was done by Kai Yin and Kang Wang. Kang Wang was responsible for writing the original draft. Kai Yin, Kang Wang, and Yun Wang were involved in reviewing, discussing, and supervising this review. All authors have reviewed and endorsed the final draft of the manuscript for publication.

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Wang, K., Wang, Y. & Yin, K. Role played by MDSC in colitis-associated colorectal cancer and potential therapeutic strategies. J Cancer Res Clin Oncol 150 , 243 (2024). https://doi.org/10.1007/s00432-024-05755-w

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Critical role of the gut microbiota in immune responses and cancer immunotherapy

• Zehua Li 1 , 2   na1 ,
• Weixi Xiong 3 , 4   na1 ,
• Zhu Liang 2 , 5   na1 ,
• Jinyu Wang 6 ,
• Ziyi Zeng 7 ,
• Damian Kołat 8 , 9 ,
• Dong Zhou 3 , 4 ,
• Xuewen Xu 1 &
• Linyong Zhao 11  

Journal of Hematology & Oncology volume  17 , Article number:  33 ( 2024 ) Cite this article

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The gut microbiota plays a critical role in the progression of human diseases, especially cancer. In recent decades, there has been accumulating evidence of the connections between the gut microbiota and cancer immunotherapy. Therefore, understanding the functional role of the gut microbiota in regulating immune responses to cancer immunotherapy is crucial for developing precision medicine. In this review, we extract insights from state-of-the-art research to decipher the complicated crosstalk among the gut microbiota, the systemic immune system, and immunotherapy in the context of cancer. Additionally, as the gut microbiota can account for immune-related adverse events, we discuss potential interventions to minimize these adverse effects and discuss the clinical application of five microbiota-targeted strategies that precisely increase the efficacy of cancer immunotherapy. Finally, as the gut microbiota holds promising potential as a target for precision cancer immunotherapeutics, we summarize current challenges and provide a general outlook on future directions in this field.

Introduction

Microbes can be found throughout the human body, from external surfaces such as the conjunctiva, oral mucosa, and skin to internal surfaces such as the gastrointestinal tract and saliva. It has been estimated that trillions of bacteria, fungi, archaea, protozoa, and viruses exist throughout the body [ 1 ]. In accordance with this fact, there is also accumulating evidence that many physiological functions within the human body, including metabolism, inflammation, and the immune response, are influenced by microbes [ 2 , 3 ]. Thanks to the technological boosts in large-scale sequencing over the past decade, multiple databases of the gut microbiome have been built to examine these functions(Table  1 ). These functions are related to the pathological processes of many human diseases, especially the development, progression, and immune evasion of cancer, as well as the modulatory effects of cancer treatments [ 4 , 5 , 6 , 7 ].

The essential properties of the gut microbiota, such as its stability, resilience, and diversity, need to be discussed, given its importance in human health [ 8 ]. The gut microbial community can be stable for years in healthy adults; thus, the microbiota has high stability. Homeostasis of the gut microbiota is maintained through negative feedback mechanisms [ 9 ]. The gut microbiota is often highly resilient to perturbations, thus allowing a host to maintain key species for long periods. However, understanding the resilience of this complex gut ecosystem is still challenging because the threshold for transitions of the gut microbiota to different states is only beginning to be determined [ 10 , 11 ]. Microbial interactions ranging from mutualism and commensalism to competition and amensalism and the symbiotic relationship between microbes and their host can be considered essential factors in shaping gut stability and resilience of the gut microbiota [ 12 ]. With the recent advent of high-throughput sequencing, the diversity of the gut microbiota has been revealed at both the species and functional levels [ 13 ]. Functional screening by shotgun metagenomics contributes significantly to understanding the functional diversity of the gut microbiome. As more complementary “omics” datasets become available, functional variation in the gut microbiota in response to disease, diet, or other factors may be discovered [ 14 ]. For studies focusing on the diversity of the gut microbiota, a key challenge is understanding functional redundancy (i.e., which community species have similar functional niches and can substitute for one another). Funtional redundancy is also a critical aspect for conferring stability and resilience to the gut microbiota [ 15 ].

The gut microbiota has been shown to play critical roles in maintaining intestinal barrier integrity and homeostasis. The composition of the gut microbiome is under the surveillance of the intestinal immune system. Inflammation caused by an imbalance between commensal and pathogenic microbes can lead to intestinal and even systemic diseases [ 16 ]. In terms of the mutually beneficial symbiotic ecosystem between the gut microbiota and the host, the host offers habitats and nutrients in the gut, while the microbes support the maintenance of lipid and glucose metabolism and the maturation of the intestinal immune system by providing microbiome-derived metabolites [ 17 ]. For instance, short-chain fatty acids (SCFAs), including acetic acid, butyric acid, and propionic acid, are essential energy sources for gut microbes and perform diverse regulatory functions related to host physiology and immunity [ 18 ]. Trimethylamine N-oxide (TMAO), which is a molecule generated from gut microbial metabolism, is also associated with host immunity [ 19 ].

Current research on the relationship between cancer and microbes has mostly focused on the gut microbiota and demonstrated a complicated interaction between the gut microbiota and the immune system; this interaction was evaluated by determining the composition of the gut microbiota [ 20 ]. For example, observations of developmental defects in germ-free (GF) mice suggest that systemic immune function may be impaired in the absence of the gut microbiota [ 21 ]. Moreover, the gut microbiota and its metabolites have been proposed to be critical factors involved in modulating the efficacy and toxicity of cancer immunotherapy. A landmark example was presented by Sivan et al. [ 22 ], who first reported the complicated crosstalk between the gut microbiota and programmed cell death protein-1 (PD-1)/PD-1-ligand 1 (PD-L1) blockade.

Consistent with the demonstrated relationships between the gut microbes and immune responses, many in vitro and in vivo studies have also noted a promising approach for optimizing the therapeutic outcomes of cancer immunotherapy: manipulating the composition of the gut microbiota [ 23 , 24 ]. However, although the concept of using the gut microbiota as a tool for precision medicine has developed rapidly over the last decade [ 25 ], the number of published studies exploring practical interventions to modify the gut microbiota is rather limited and unspecific. In this review, we will discuss five commonly explored interventions that have had relatively strong impacts on the therapeutic outcomes of cancer immunotherapy, namely, fecal microbiota transplantation (FMT), diet, probiotics, prebiotics, and engineered microbial products. Compared with the other four methods, FMT is a well-established clinical approach recommended by the FDA for modulation of the gut microbiota. The gut microbes from a healthy host are transplanted to recover microbial homeostasis in the recipient. However, the research has been restricted to correlation relationships rather than causality, and outlining the future direction of clinical applications utilizing the gut microbiota is challenging. With multiomics tools and synthetic biology, we can now explore the exact mechanism underlying gut microbiota modification in cancer immunotherapy. Here, we will also provide evidence to support the incorporation of gut microbiota modification in immunotherapy while acknowledging the challenges in this rapidly developing field.

The interplay between the immune system and the gut microbiota

Gut microbiota symbiosis plays a multifaceted role in shaping the immune responses of the human host [ 26 , 27 ]. This complicated crosstalk allows for the normal functioning of immune tolerance and immunosurveillance, which recognizes and eliminates opportunistic bacteria to prevent potential infection. The critical role of the gut microbiota in the formation of a fully functional immune system was identified in GF animals [ 28 ]. As a go-to animal model for bacteria-host interactions, GF animals display distinct features in the gut, including an immature mucus system, unformed gut-associated lymphoid tissues, and a reduced number of immune cells [ 29 , 30 , 31 , 32 , 33 ]. Here, we summarize the current views on how the gut microbiota influences various components of the systemic immune system. We roughly divided the following discussion into three parts: non-gastrointestinal (GI) tract lymphoid organs, the innate immune system, and adaptive immune system components in the GI tract. Specifically, we summarize the interactions between immune cells and gut microbiota (Table  2 ).

Lymphoid organs

Regarding the interplay of non-GI tract lymphoid organs with the gut microbiome, several studies have revealed immunological modulation by microbes in the thymus, bone marrow, and spleen. Initial clinical evidence showed an association between primary lymphoid organs and the gut microbiota in patients with hematologic malignancies [ 34 , 35 ]. This association was further validated with mouse models by Staffas et al. [ 36 ], where depletion of the gut microbiota led to significant reductions in lymphocyte and neutrophil counts. Moreover, metabolites such as SCFAs can facilitate the recovery of hematopoiesis in bone marrow after radiation damage [ 37 ]. The developed bone marrow can work together with translocated gut microbiota to drive the expansion of yolk sac-derived macrophages, increase the number of granulocytes and monocyte progenitors, and promote their differentiation [ 38 ]. In addition, bone marrow development can also be affected by peptidoglycans, which modulate neutrophil function [ 39 ]. In the thymus, studies have demonstrated that recolonization of the gut microbiota drives the thymic expansion of T cells. Specifically, the gut microbiota is trafficked to the thymus in a CX3CR1- and CCR5-dependent manner by intestinal CX3CR1 DCs, which assist in inducing the expansion of microbiota-specific T cells [ 40 ]. Researchers have demonstrated that cyclophosphamide (CTX) induces the translocation of selected bacteria into the spleen, followed by the stimulation of a specific subset of “pathogenic” helper T (Th) 17 cells, which generate memory Th1 immune responses and increase the CD8 + /Regulatory T(Treg) cell ratio [ 41 , 42 ] (Fig.  1 ).

The interplay between the immune system and the gut microbiota in non-GI tract lymphoid organs. The gut microbiota and its metabolites influence the development of host bone marrow and thymus. For instance, SCFAs are capable of facilitating hematopoiesis recovery of bone marrow after radiation damage.The gut microbiota also induce the translocation of selected bacteria into and stimulate immunocytes and immune responses of the spleen after CTX treatment

Antimicrobial peptides (AMPs)

AMPs are secreted by epithelial cells in the gut, mostly Paneth cells [ 43 ]. They are a crucial component of immunoreactive substances, and affect the innate immune system. As the first-line defender, AMPs modulate the immune system in response to a wide range of invasive pathogens. The most abundant AMPs, human defensin(HD) HD-5 and HD-6, modulate the microbiota in vivo via an increase in the abundance of Akkermansia sp [ 44 ]. In mouse models, the lack of pore-forming Orai1 was associated with high mortality due to severe intestinal bacterial dysbiosis, and the absence of AMP secretion from acinar cells was considered the major cause [ 45 ] (Fig.  2 ).

The interplay between the innate immune system and the gut microbiota in GI tract. Some mechanisms utilized by the gut microbiota to interact with the host innate immune system in GI tract are described above. The interplay between the gut and its microbiota is complex. The secretion of AMPs could be affected by A.muciniphila . PRRs are strongly affected by the presence of the gut microbiota. Microbiota-derived TLR and NOD ligands act directly on intestinal immunocytes and can activate inflammatory genes. Bacteroides fragilis stimulates the downstream PI3K pathway and activates the transcription of anti-inflammatory genes by co-operating TLR1/TLR2 heterodimer and Dectin-1. NLRs function to activate inflammatory caspases and cytokines to compost optimal microbiota and maintain intestinal homeostasis. Microbial metabolites taurine, histamine, and spermine have been identified to regulate the activation of NLRP6 inflammasome as well as the induction of downstream epithelial IL-18 and AMPs secretion. Innate immune cells, including macrophages, DCs, and NK cells, interact heavily with the gut microbiota. OMVs derived from Bacteroides elicit IL-10 production by DCs, as well as enhance the phagocytic functions of macrophages triggered by the bacteria themselves. The expression of the transcription factor RORγt and IL-22 of intestinal NK cells is conditioned by the commensal microbiota

Pattern recognition receptors (PRRs)

PRRs identify host receptors that recognize specific pathogen-associated molecular patterns (PAMPs), making PRRs a critical factor in defense against infectious pathogens [ 46 ]. Following activation by PAMPs, PRR signaling pathways produce AMPs, cytokines, chemokines, and apoptotic factors. These factors are expressed not only in innate immunity but also in various nonprofessional immune cells, such as intestinal epithelial cells (IECs) in the GI tract. The most well-studied PRRs are toll-like receptors (TLRs) and nucleotide oligomerization domain (NOD)-like receptors (NLRs) [ 47 ]. Understanding how microbes influence PRR-associated immune responses is fundamental for understanding gut microbiome homeostasis.

TLRs are widely expressed in the GI tract but differ significantly between the intestine and colon [ 48 ]. We focused on TLR4, TLR5, TLR9, and TLR2, which are involved in microbe recognition. In the context of the GI tract, TLR2 is expressed in mononuclear cells of the lamina propria, goblet cells, and enterocytes. TLR4 and TLR9 are expressed mainly in IECs [ 49 ]. In addition, TLR5 is expressed on the basolateral side of IECs in the colon, while its expression is restricted to Paneth cells in the small intestine [ 50 ]. TLRs are strongly affected by the presence of microbes [ 51 ]. In particular, we will discuss how TLR signaling mediates the crosstalk between microorganisms and IECs and how this structural and functional interplay primes immune cell responses in the gut mucosa. Microbial metabolites strongly regulate IEC proliferation, apoptosis, and differentiation [ 52 ]. These processes can be induced by the development of goblet cells that are activated by TLR2 and TLR4 [ 53 ]. The motility of intestinal smooth muscle could be another factor that impacts the differentiation of IECs, which is mediated by TLR4, TLR5, and TLR9 [ 54 , 55 ]. Researchers have revealed that TLR2 stimulation effectively preserves tight junction-associated barrier integrity by promoting phosphoinositide 3-kinase (PI3K)/Akt-mediated cell survival via myeloid differentiation primary response gene 88 (MyD88) as well as the translocation of zona occludens 1 (ZO1) and occluding proteins [ 56 ]. Moreover, activation of TLR4 induces a loss of barrier function through the expression of myosin light chain kinase (MLCK) [ 57 ]. In addition, AMP and IgA transcytosis are highly dependent on TLR-mediated recognition of the gut microbiota [ 58 , 59 ]. IECs control microbial invasion of the mucosa through the release of ROS into the lumen after TLR activation [ 60 ]. These results indicate that TLRs are involved in intercellular junctions, and that enhancing or disrupting intestinal epithelial barrier integrity depends on microbes. A typical example for understanding TLR–microbe interplay is the symbiont molecule polysaccharide A (PSA) of  Bacteroides fragilis (B.fragilis) . PSA interacts with the TLR1/TLR2 heterodimer on DCs in cooperation with Dectin-1 to stimulate the downstream PI3K pathway, followed by the transcription of anti-inflammatory genes. This PSA-dependent immunomodulation is essential for presenting CD4 + T cells and Treg cells, which are critical for producing interleukin-10 (IL-10), which is the primary anti-inflammatory outcome [ 61 , 62 ].

NLRs activate inflammatory caspases and cytokines and modulate inflammatory signaling pathways [ 63 ]. NOD1/NOD2 recognizes peptidoglycan in bacterial cells and activates the NF-κB/extracellular-signal-regulated kinase(ERK) /mitogen-activated protein kinase(MAPK) signaling pathway to mediate cytokine, chemokine, and antimicrobial peptide expression, thereby promoting the host immune response [ 64 , 65 , 66 ]. Specifically, stimulation of epithelial cells with NOD1 stimulatory molecules can induce the production of CXCL1, CCL2, IL-8, and AMPs, which are essential for recruiting neutrophils [ 67 ]. In NOD2(-/-) mice, inflammatory pathologies associated with the expansion of Bacteroides vulgatus were observed [ 68 ]. Researchers confirmed that NOD2 mediates CCL2-CCR2-dependent recruitment of inflammatory monocytes and promotes their production of IL-10 [ 69 ]. Moreover, the anti-inflammatory effects of Lactobacillus salivarius Ls33 were abrogated in NOD2(-/-) mice [ 70 ]. NOD-like receptor thermal protein domain associated protein(NLRP)3, plays a well-defined role in intestinal homeostasis and protection against inflammation [ 71 ]. According to Seo et al. [ 72 ], Proteus mirabilis ( P. mirabilis ) can induce robust IL-1β release by meditating the recruitment of CCR2 mononuclear phagocytes. Similarly, Yao et al. [ 73 ] confirmed that the hyperactive NLRP3 inflammasome could remodel the gut microbiota by inducing IL-1β production. Furthermore, they observed enhanced production of AMPs and compensatory changes in local Treg cell levels to neutralize inflammation. Another well-studied inflammasome-forming NLR is NLRP6. Elinav et al. [ 74 ] described the novel regulatory mechanism of the NLRP6 inflammasome in which a deficiency of NLRP6 resulted in reduced IL-18 and IL-1β levels. Additionally, NLRP6 knockout mice had an increased abundance of Akkermansia muciniphila (A.muciniphila) [ 75 ]. Wlodarska et al. [ 76 ] further explored the regulatory effect of the NLRP6 inflammasome on the biogeographical distribution of the gut microbiota, and the authors suggested that NLRP6 mediates mucin granule exocytosis and subsequent mucous layer formation. In another study, Levy et al. [ 77 ] reported that taurine, histamine, and spermine activated NLRP6 inflammasome and induced downstream epithelial IL-18 and AMP secretion. In addition to inflammasome formation, NLRP12 suppresses NF-κB signaling and the expression of downstream inflammatory cytokines [ 78 , 79 , 80 , 81 ]. Two recent studies have connected NLRP12 with the gut microbiota in the contexts of colon inflammation and obesity. Chen et al. [ 82 ] found that microbial dysbiosis contributed to colitis in NLRP12 knockout mice. These mice exhibited increased expression of inflammatory cytokines, including tumor necrosis factor-α(TNF-α) and IL-6, by DCs, which was reversed by the administration of Lachnospiraceae . In addition, inflammation associated with obesity in NLRP12-deficient mice was attributed to the maintenance of beneficial microbiota [ 83 ] (Fig.  2 ).

Macrophages

Macrophages are known as the first-line of defense against pathogens, but they also interact heavily with commensal bacteria [ 84 ]. B. fragilis enhances the phagocytic functions of macrophages by polarizing them to an M1 phenotype [ 85 ]. Researchers have shown that the gut microbiota promotes the interaction between IL-1β–secreting macrophages and colony-stimulating factor 2 (Csf2)-producing RORγt + innate lymphoid cells 3 (ILC3s) [ 86 ]. Several studies have explored the influence of microbial products on macrophages. By inhibiting the release of NO, IL-6, and IL-12, n-butyrate may assist in the tolerance of colon macrophages to commensals [ 87 ]. Furthermore, butyrate-enhanced antimicrobial activity was shown to be related to alterations in macrophage metabolism and increased LC3-associated antimicrobial clearance [ 88 ]. TMAO-polarized inflammatory macrophages induce a potent Th1 and Th17 response by modulating the microenvironment, which exacerbates inflammation-related diseases [ 89 ] (Fig.  2 ).

Dendritic cells (DCs)

DCs are the most potent and versatile professional antigen-presenting cells (APCs), that can initiate the adaptive immune response and support innate immunity [ 90 ]. DCs can be divided into plasmacytoid DCs (pDCs) and conventional DCs (cDCs) [ 91 , 92 ]. Researchers have suggested that cDCs cannot be fully activated due to insufficient interferon-I(IFN‐I) signaling. In other words, the gut microbiota, which is the major regulator of IFN-I secreted by pDCs, controls the basal state of DCs [ 93 ]. Another example of this crosstalk is the outer membrane vesicles (OMVs) derived from Bacteroides thetaiotaomicron . These OMVs are instrumental in eliciting regulatory IL-10 production by DCs [ 94 ]. In addition, Bessman et al. [ 95 ] reported that hepcidin produced by cDCs in response to microbiota-derived signals promoted intestinal homeostasis. (Fig.  2 ).

Natural killer (NK) cells

NK cells are an important component of the innate immune system and account for up to 15% of all lymphocytes [ 96 ]. Researchers have suggested that the innate mucosal defense provided by a subset of intestinal NK cells is conditioned by the commensal microbiota, which expresses the transcription factors RORγt and IL-22 [ 97 ]. Four trials applying synbiotics or probiotics have shown that administration improved the gut microbiota composition and increased NK cell activity and the levels of associated cytokines [ 98 , 99 , 100 , 101 ]. More specifically, Qiu et al. [ 102 ] reported that the probiotic Lactobacillus plantarum can efficiently increase the expression of IL-22 mRNA and protein in NK cells, thereby mitigating intestinal epithelial barrier damage. (Fig.  2 ).

B cells are crucial mediators of intestinal homeostasis. By secreting immunoglobulins and cytokines, they assist in maintaining a noninflammatory host-microbe relationship [ 103 , 104 ]. GF mice show a reduced amount of immunoglobulin A, a differentiated form of B-cell, and impaired B-cell responses [ 105 ]. The intestinal colonization of E. coli , bifidobacteria , and segmented filamentous bacteria (SFB) might promote B-cell maturation and enhance the specific IgA antibody response [ 106 , 107 ]. This IgA response helps maintain gut microbiota homeostasis, thereby facilitating the expansion of Foxp3 + T cells and maturation of the gut immune system through a symbiotic regulatory loop [ 108 ]. The regulation of B cells by the gut microbiota and its products could be influenced by IgA, immune cells, chemokines, cytokines, or even B cells themselves [ 109 ]. More specifically, B-cell activating factors can be induced by IECs, DCs, T cells, and eosinophils. Together, these immune cells and cytokines can promote the differentiation and survival of IgA plasma cells [ 110 , 111 , 112 , 113 , 114 ]. Additionally, microbial metabolites such as SCFAs activate B-cell receptors (BCRs), inhibit histone deacetylases (HDACs), and increase adenosine triphosphate (ATP) levels [ 115 , 116 ]. The differentiation of naïve B cells into regulatory B cells (Bregs) can be induced by intestinal microbiota-driven production of IL-1β and IL-6 [ 117 ] (Fig.  3 ).

The interplay between the adaptive immune system and the gut microbiota in GI tract. Some mechanisms utilized by the gut microbiota to interact with the host innate immune system in GI tract are described above. Foxp3 + Treg cells promote maturation of B cells and production of secretary IgA. These contribute to the regulation of homeostatic microbiota composition and the maintenance of a non-inflammatory host-microbial relationship. CD8 + T cells can be activated by the intestinal microbiota and its metabolites. Butyrate, for instance, showed a direct antagonistic influence on the HDAC of CTLs and Tc17 cells, thereby promoting the expression of IFN-γ and granzyme B. As for Th cells, the adhesion of SFB to IECs is a common outcome of inducing homeostatic intestinal Th17 cells. Tfh cells, being another modulation target of gut microbiota modification, are essential for the production of plasma cells and memory B cells. The SCFAs have been demonstrated to regulate the size and function of the Treg cell pool

CD8 + T cells

T cells coordinate the immune response and directly kill damaged cells. These functions are mediated by CD4 + and CD8 + T cells, respectively. CD8 + T cells play central roles in controlling infections and cancer. These cells are known to secret IFN-γ and the protease granzyme B, which act synergistically to kill infected or tumorigenic cells [ 118 ]. CD8 + T cells can be activated by the intestinal microbiota and its metabolites, such as cytotoxic T lymphocytes (CTLs), to exert direct cytotoxicity and interact with other immune cells, especially in the tumor microenvironment (TME) [ 119 ]. Conversely, microbial dysbiosis exacerbates chronic inflammation and tumor susceptibility, thereby attenuating the activity of CD8 + T cells and sometimes even causing their exhaustion [ 120 , 121 , 122 , 123 ]. Moreover, butyrate had a direct antagonistic influence on the HDACs of CTLs and cytotoxic T lymphocyte 17 (Tc17) cells, thereby promoting the expression of IFN-γ and granzyme B [ 124 ]. Butyrate could also promote activated CD8 + T cell differentiation into memory cells [ 125 ]. Immunotherapy targeting the close interaction between CD8 + T cells and the gut microbiota is promising and will be discussed below (Fig.  3 ).

Helper T (Th) cells

Th cells, which are differentiated from naïve CD4 + T cells, can orchestrate humoral and cellular immunity by facilitating the activation of immunocytes in a cytokine-dependent manner [ 126 , 127 ]. Different subsets of Th cells show distinct functions in protective immunity and reactivity to the gut microbiota because of differences in the production of signature cytokines [ 128 ]. Th1 cells produce IFN-γ, IL-2, and TNF-α, and the expression of IL-4, IL-5, and IL-13 defines Th2 cells. Th17 cells are abundant within the GI tract and help regulate gut microbes. The signature cytokines of this cell subset include IL-17A, IL-17F, and IL-22 [ 129 ]. Th1 and Th2 cells exhibit functions that are regulated by the gut microbe-derived metabolites [ 130 ]. SCFAs are associated with an impaired ability to initiate a Th2 cell immune response [ 131 ]. Additionally, SCFAs can promote microbe antigen-specific IL-10 production in Th1 cells through GPR43 and induce the expansion of the Th1 transcription factor T-bet [ 132 ]. Furthermore, cancer patients display decreased plasma tryptophan(Trp) levels correlated with an increase in Th1-type immune activation markers [ 133 ]. The potential association between Th17 cells and gut microbes has been shown in different diseases. Specific alterations in the intestinal mucosa-associated microbiota were correlated with an increased number of intestinal Th17 cells and a high disease burden [ 134 ]. Preclinical models further verified this correlation by showing that augmenting the population of pathogenic colonic Th17 cells could promote tumorigenesis [ 135 ]. However, their causal relationships have not been proven. We propose that the delicate balance of plasticity makes Th17 cells potential pathogenic drivers of intestinal immune diseases [ 136 , 137 , 138 , 139 , 140 , 141 ]. Studies have shown that the gut microbiota and metabolites activate Th17 cells. The impaired plasticity of Th17 cells in the absence of the gut microbiota can be restored by microbial metabolites [ 142 , 143 , 144 ]. SFB is a representative example of a molecule that can induce homeostatic intestinal Th17 cells [ 145 , 146 ]. Atarashi et al. [ 147 ] further demonstrated that the adhesion of SFB to IECs is a critical factor for inducing Th17 cells and antigen binding to pro-Th17 DCs. Another study revealed that Bifidobacterium adolescentis could influence Th17 cells in a similar manner [ 148 , 149 , 150 ]. Researchers have shown that ATP derived from commensal bacteria can activate a unique subset of lamina propria cells, namely, CD70high/CD11clow cells, which induce IL-6 and transforming growth factor(TGF)-β, leading to the differentiation of Th17 cells [ 151 ]. Moreover, different gut microbe-derived BA and SCFA metabolites regulate and modulate Th17 cell immunological function and differentiation [ 152 , 153 ]. Various diets have also been shown to have complicated impacts on Th17 cells [ 154 , 155 ] (Fig.  3 ).

Follicular helper T (Tfh) cells

Another critical subset of Th cells is Tfh cells. In addition to assisting B cells in producing antibodies, Tfh cells are essential for germinal center (GC) formation, affinity maturation, and the production of memory B cells [ 156 ]. The maturation of Tfh cells is restricted in GF mice, resulting in diminished IgA development and disruptions in microbial homeostasis [ 111 ]. Alterations in the gut microbiota can be observed in Tfh cells when ATP-gated ionotropic P2X7 receptors are absent [ 157 , 158 ]. Moreover, bacteria of the genus Anaeroplasma can increase intestinal IgA levels by inducing TGF-β in Tfh cells [ 159 ] SFB can induce the differentiation of Tfh cells and egress into systemic sites, thereby facilitating systemic Tfh cell responses and autoantibody secretion that can worsen diseases [ 160 ]. Microbiota-derived eATP can also regulate Tfh cell abundance [ 161 ]. Thus, the gut microbiota can be a modulatory target of Tfh cells to further impact intestinal immunity [ 162 ] (Fig.  3 ).

Some Treg cells are also found in B-cell follicles and were identified as T follicular regulatory (Tfr) cells. These cells can migrate into the GC, thereby inhibiting B-cell maturation and antibody production [ 163 ] SFB, which induces Tfh cells to promote autoimmune arthritis, has also exhibited the potential to influence systemic Tfr cells [ 164 ]. In addition, butyrate is an environmental cue that can induce the differentiation of Tfr cells, which can also ameliorate autoimmune arthritis [ 165 ].

Regulatory T (Treg) cells

Treg cells, which differentiate from naïve CD4 + T cells, are an irreplaceable constituent of immunity and are involved in the maintenance of immunological self-tolerance and homeostasis. Treg cells express the transcription factor Foxp3 in the nucleus and CD25 and CTLA-4 on the cell surface [ 166 ]. These factors are modulated by gut microbial signals [ 167 , 168 , 169 , 170 ]. TGF-β, the physiological inducer of the transcription factor Foxp3 (associated with the development of Treg cells), can be induced by Clostridia [ 171 , 172 ] B. fragilis has been shown to form OMVs, packed with capsular PSA, and increase IL-10 expression in Treg cells, and activate TLR2 ligation on T cells and DCs [ 173 , 174 ]. SCFAs have been demonstrated to regulate the size and function of the Treg cell pool [ 175 , 176 ]. Specifically, butyrate promotes histone H3 acetylation at the Foxp3 locus, and propionate inhibits HDACs [ 177 , 178 ].

In summary, microbes exert positive and negative effects on the immune system of the GI tract, thus indicating their dual role in cancer progression. Gut microbiome homeostasis enhances the host immune response. However, dysbiosis and depletion of the gut microbiome interfere with the immune system abnormally by manipulating various innate and adaptive immune system components, which may further increase susceptibility to tumorigenesis. (e.g., inducing a loss of intestinal barrier function through the PRR signaling pathway; affecting B-cell differentiation and response; attenuating CD8 + T cells, even causing their exhaustion; causing impaired plasticity in Th17 cells; and restricting the maturation of Tfh cells). Specifically, different strains of gut microbes play different roles in regulating GI tract immunity. In the GI tract, A.muciniphila , B.fragilis , Ls33 , Lachnospiraceae , E. coli , bifidobacterial , SFB , and Bifidobacterium adolescentis are associated with immune cell activation processes and exhibit anti-inflammatory properties. Moreover, strains like Bacteroides vulgatus displayed inflammatory pathologies, which might be involved in cancer progression. Microbial metabolites showed similar dual characteristics. Butyrate attenuates the inflammatory response, while TMAO promotes it.

The gut microbiota and the efficacy of cancer immunotherapy

The idea of cancer immunotherapy has evolved rapidly in the past few decades. Many types of immunotherapy have been developed to revive the immune system by suppressing the immunoinhibitory pathways commonly employed by tumor cells to escape immunosurveillance. A close link between the gut microbiota and cancer immunotherapy has slowly been unveiled with an increasing number of innovative studies. We outline the recent evidence in this field by type of immunotherapy (Additional file 1 : Table S1) (Fig.  4 ).

Selected mechanisms of how the gut microbiota impact cancer immunotherapies. Current studies have revealed the close link between the gut microbiota and the efficacy of cancer immunotherapy. Grouped by immunotherapies and metabolites, outlined here are some selected mechanisms utilized by the gut microbiota and its metabolites to regulate immunocyte activation, cytokine secretion, metabolism restriction and tumor cell proliferation inside the TME to influence cancer immunotherapy effects

Antibodies against PD-1/PD-L1

PD-1 is a coinhibitory transmembrane receptor expressed on tumor-infiltrating lymphocytes (TILs) [ 179 ]. Within the TME, PD-1 binds to PD-L1 and consequently inhibits CTL-mediated cytolysis, as well as Fas-induced cellular apoptosis, thus allowing tumor cells proliferate indefinitely [ 180 , 181 ]. Inhibitors of PD-1/PD-L1, such as nivolumab, pembrolizumab, and atezolizumabor, promote immune responses against cancer cells in clinical trials [ 182 , 183 , 184 , 185 , 186 , 187 ].

Moreover, landmark experiments have confirmed the association between antibodies against PD-1/PD-L1 and the gut microbiota. These preclinical trials have explored the hallmark mechanisms of this crosstalk: (1) alterations in the gut microbiota composition caused by immune checkpoint inhibitors(ICIs), (2) the effects of gut microbes on intestinal immune cells, (3) induced metabolic changes affecting the immune response of commensals, and (4) the accumulation of immunocytes in the TME caused by the gut microbiota. Specifically, this crosstalk was first explored by Sivan et al. [ 22 ]. Their data suggested that Bifidobacterium could augment DC functions and enhance CD8 + T-cell priming and accumulation in the TME. Routy et al. [ 188 ] confirmed the correlation between the abundances of different microbes ( A.muciniphila and E.hirae ) and PD-1/PD-L1 blockade efficacy. Mechanistically, these researchers demonstrated that the antitumor effect was restored in an IL-12-dependent manner by increasing the recruitment of CCR9 + CXCR3 + CD4 + T lymphocytes into the TME. Another study indicated that Prevotella and A.muciniphila improved the therapeutic efficacy of PD-1/PD-L1 inhibitors and Bacteroides led to poorer efficacy. Researchers have speculated that changes in the gut microbiota affect glycerophospholipid metabolism, thereby altering the expression of IFN-γ and IL-2 in the TME [ 189 ]. In mice with breast cancer (BC), anti-PD-1 therapy increased the abundance of Bifidobacterium , Lactobacillus , and Adlercreutzia [ 190 ].

Analogous clinical studies were implemented in the following years, and the results validated the correlation between the gut microbiota composition and the therapeutic efficacy of ICIs in clinical trials beyond preclinical models.

In trials involving metastatic melanoma (MM) patients, contradictory results showed that no single species could be regarded as an entirely consistent predictive factor. In terms of mechanism, Gopalakrishnan et al. [ 191 ] reported increased abundances of Clostridiales , Ruminococcaceae , and Faecalibacterium in responders(R) and suggested that increasing antigen presentation and improving effector T-cell function in the TME could enhance antitumor immune responses. Matson et al. [ 192 ] performed FMT to transfer R-enriched bacteria into colonized mice and observed an increased frequency of DCs and augmented T-cell responses. Other studies have shown that specific bacterial species are associated with R and nonresponders(NRs) [ 193 , 194 ] and that carriers of specific bacterial taxa exhibit a better cancer prognosis [ 195 , 196 ].

Multiple studies on the systemic immune responses of cancer patients ranging from those with melanoma to those with non-small cell lung carcinoma (NSCLC) have detected a greater frequency of memory CD8 + T cells and NK cells in the periphery of R enriched with Alistipes putredinis Bifidobacterium longum , and Prevotella copri [ 197 ]. A group in the United States found that mice model with transplanted gut microbes had improved ICI efficacy when the TME was enriched with immunocytes [ 198 ]. Other studies have also demonstrated a diverse array of molecular features in the gut microbiota during immunotherapy modulation [ 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 ]. Taken together, the findings are conflicting; thus, continued research efforts are needed to establish causal relationship between different microbes and ICI treatment efficacy. Similarly, studies focusing on other rare thoracic malignancies are needed, although initial data have been provided [ 207 ].

Not until 2019 did studies start focusing on predicting responses to PD-1/PD-L1 immunotherapy based on the gut microbiota composition in the context of hepatocellular carcinoma (HCC). Zheng et al. [ 208 ] reported that the dynamic nature of commensals plays an important role in ameliorating oxidative stress injury and host inflammatory responses in antitumor therapy. Another study revealed that the antitumor functions of certain bacterial species could be a result of SCFA production and bile acid metabolism [ 209 ]. Although multiple studies have demonstrated that better ICI efficacy in HCC patients appears to be correlated with a favorable gut microbiota [ 210 , 211 , 212 ], one recent study failed to confirm such a positive association in patients with HCC [ 213 ].

Compared with those of the solid tumors mentioned above, little is known about the direct impact of individual intestinal nonpathogenic bacteria on the therapeutic outcomes of ICIs in renal cell carcinoma (RCC). Derosa et al. [ 214 ] observed a positive association between D. formicigenerans and CD8 + CD69 + T cells as well as negative associations between C. clostridioforme and CD137/4.1BB expressing CD4 + T lymphocytes and memory CXCR5-CCR6-CCR4-CCR10-CXCR3 + CD8 + T cells. Salgia et al. [ 215 ] also identified several species that were presumably correlated with therapeutic benefits.

Although a significant amount of research has been dedicated to revealing how the gut microbiota influences the carcinogenesis of colorectal carcinoma (CRC), little is known about the regulatory mechanisms involved in the efficacy of ICIs. In a recent study, F. nucleatum was connected to the activation of the stimulator of interferon genes (STING) signaling pathway as well as the accumulation of IFN-γ + CD8 + TILs [ 216 ]. To better understand how individual bacterial species modulate ICI therapy, future studies are needed to better characterize any shared functionalities among different microbial communities.

The negative impact of H. pylori on immunomodulation raises the concern that H. pylori infection may suppress immune responses to cancer immunotherapy [ 217 , 218 ]. Researchers have confirmed that H. pylori infection decreases the effectiveness of cancer immunotherapies by inhibiting DCs and suppressing CD8 + T-cell responses [ 219 ].

Antibodies against cytotoxic T lymphocyte-associated antigen 4 (CTLA-4)

CTLA-4 is a major negative receptor of T cells and has upregulated expression upon T-cell activation [ 220 , 221 , 222 , 223 , 224 , 225 , 226 ]. Inhibitors of CTLA-4, such as ipilimumab and tremelimumab, are thought to boost antitumor immunity due to the strong immunosuppressive effects of CTLA-4 [ 227 , 228 , 229 , 230 , 231 ]. Mechanistically, anti-CTLA-4 blockade affects the Th1 subset of CD4 T cells that express an inducible costimulator (ICOS) [ 232 , 233 ]. Additionally, both effector T cells and Tregs are the primary targets of anti-CTLA-4 mediated blockade [ 234 , 235 ].

Studies have revealed the mechanisms by which different species of gut microbiota improve the clinical outcomes of anti-CTLA-4 immunotherapy. Initially, an altered gut microbiota was thought to activate IL-12-dependent Th1 immune responses, thereby facilitating antitumor effects [ 236 , 237 ]. Chaput et al. [ 238 ] confirmed that prolonged progression-free survival (PFS) and overall survival (OS) in patients enriched with Firmicutes was mediated by increased ICOS induction levels of CD4 + T cells and sCD25 levels. A recent study suggested that the antitumor efficacy of CTLA-4 blockade is negatively correlated with the proportion of the microbial metabolite butyrate since systemic butyrate is capable of inhibiting ipilimumab-mediated DC maturation and the CD28 signaling pathway (Additional file 1 : Table S1) [ 239 ].

Adoptive cell transfer (ACT)

While ICI efficacy relies on the presence of tumor-reactive T cells [ 240 ], ACT may be a good strategy for treating poorly immunogenic types of cancer [ 241 ]. There are two approaches to ACT: (1) isolating TILs from the TME and (2) genetically modifying blood-derived T cells to express chimeric antigen receptor (CAR). Both approaches require in vitro T-cell manipulation before reinfusion into patients [ 242 , 243 , 244 , 245 , 246 , 247 ]. Considering the obstacles to the application of ACT, interventions modulating the immune microenvironment, such as gut microbiota modifications, have become a central issue [ 248 , 249 ].

Paulos et al. [ 250 ] reported for the first time that translocated microbes could augment the function of ACT therapy by triggering the TLR4 pathway. Activating this pathway stimulates DCs and increases the secretion of proinflammatory cytokines in the gut. Similarly, other studies also revealed enhanced ACT efficacy after vancomycin supplementation, which induced IL-12 expression to increase the number and activity of tumor-specific TILs [ 251 ]. Adoptive transfer of naïve Helicobacter hepaticus ( Hhep )-specific CD4 + T cells has been shown to contribute to antitumor immunity in CRC. Mechanistically, researchers have discovered that increased Hhep levels stimulate tertiary lymphoid structures (TLSs), which further activate NK cells and CD4 + T cells [ 252 ]. Recently, Smith et al. [ 253 ] demonstrated a close correlation between a high abundance of Ruminococcus , Bacteroides , and Faecalibacterium and better responses to CD19 CAR T-cell therapy in patients. Collectively, these findings, although preliminary, have not revealed the exact mechanisms by which bacterial taxa and metabolites influence ACT immunotherapy outcomes, especially CAR-T-cell therapy outcomes (Additional file 1 : Table S1) [ 254 ].

Unmethylated cytidine phosphate guanosine oligonucleotide (CpG-ODN) therapy

CpG-ODNs possess immunostimulatory effects and potential antitumor activity [ 255 ]. They interact with TLR9 in B cells and plasmacytoid DCs to initiate a signaling cascade that activates the NF-κB pathway and various cell types and induces the production of cytokines and chemokines [ 256 ]. Thus, CpG-ODN injections were initially promoted for their immunotherapeutic potential, and recent studies have focused on applying CpG-ODNs as an adjuvant to other cancer treatments [ 257 , 258 , 259 ].

Iida et al. [ 119 ] identified several species associated with CpG-ODN efficacy. These associations suggest that the gut microbiota affects immunotherapy by inducing TNF production and manipulating tumor-associated myeloid cells. These findings confirmed that commensals affect the outcomes of patients receiving CpG-ODN therapy by regulating inflammatory responses in the TME (Additional file 1 : Table S1).

Microbial metabolites and the efficacy of cancer immunotherapy

Metabolites derived from the gut microbiota have been identified as important regulators of the development and function of immune cells [ 17 , 260 , 261 ]. Given their complicated interactions with the immune system, multiple studies have focused on how they impact local and systemic antitumor immune responses, especially in the context of ICI therapy (Fig.  4 ). These heavily studied metabolites can be divided into three subgroups according to their origin and synthesis: (1) metabolites produced by the gut microbiota from dietary components, (2) metabolites produced by the host and modified by the gut microbiota, and (3) metabolites synthesized de novo by the gut microbiota. We will discuss the latest evidence about the potential mechanisms underlying these interactions for each of these groups.

Metabolites produced by the gut microbiota from dietary components

In the intestine, dietary fiber can be fermented into SCFAs by the gut microbiota [ 262 ]. These SCFAs act as signaling molecules that regulate host physiology and immune processes, specifically by inhibiting HDACs or activating G protein-coupled receptors (GPRs) [ 87 , 263 , 264 , 265 , 266 ]. Multiple studies have confirmed the association between gut microbiota-derived SCFAs and the long-term benefits of ICI treatment in cancer [ 202 , 267 , 268 , 269 ]. However, Coutzac et al. [ 239 ] identified the antagonist effect of SCFAs that limits anti-CTLA-4 activity. Here, we will discuss the critical role that SCFAs play in the immune system, which demonstrates their antitumor effects in cancer immunotherapy.

SCFAs directly inhibit the proliferation of tumor cells. Researchers have shown that butyrate can inhibit tumor cell proliferation by decreasing the activation of nuclear factor of activated T-cell (NFAT)c3 and calcineurin [ 267 ]. Additionally, propionate produced by A. muciniphila promotes tumor cell apoptosis [ 268 ] In addition, SCFAs can induce histone hyperacetylation by inhibiting HDACs, leading to cell cycle arrest [ 269 ].

Moreover, SCFAs activate immune cells to augment antitumor immune responses. SCFAs can modulate intestinal macrophages and DCs through the inhibition of HDACs [ 87 , 265 , 270 , 271 ]. Research has also shown that SCFAs modulate the suppressive function and differentiation of Foxp3 + Treg cells in an HDAC-dependent manner to establish immunological homeostasis in the gut [ 175 , 177 , 178 , 272 ]. Singh et al. [ 273 ] showed that the GPR-butyrate interaction is another signaling factor that is involved in the differentiation of Treg cells. SCFAs also improved the efficacy of anticancer therapy by influencing cytotoxic CD8 + T cells. The antitumor effect was boosted by the inhibition of class I HDAC enzymes via an IL-12-dependent signaling pathway [ 274 , 275 ]. The metabolic promotion of glycolysis and oxidative phosphorylation in CD8 + T cells induced by SCFAs provides energy for immune cells [ 276 ]. In addition, SCFAs increase acetyl-CoA levels to modulate energy metabolism in B cells to support antibody production [ 112 ].

There are also contradictory findings showing restricted antitumor activity of anti-CTLA-4 in the face of high systemic levels of butyrate [ 239 ], leading to poor clinical response to treatment with ICIs. Although the mechanism through which SCFAs affect the efficacy of ICIs remains ambiguous, the SCFA-associated immunomodulatory pathway and its relevant clinical trials are still a promising area of research.

Tryptophan catabolites

Tryptophan catabolites, which mostly result from the degradation of dietary proteins, are critical contributors to intestinal and systemic homeostasis [ 277 ]. These proteins act as ligands for the aryl hydrocarbon receptor (AhR) [ 278 ], which is a ligand-inducible transcription factor in host cells that assists in immune responses [ 279 , 280 ]. Accumulating evidence has confirmed the antitumor effect of targeting these microbial metabolites in cancer treatment.

Clinical research has shown that a decreased ratio of serum kynurenine(Kyn)/ Trp improves ICI treatment efficacy [ 281 , 282 ]. In concert, studies have further demonstrated that T-cell proliferation can be inhibited by high Kyn/Trp ratios, which consequently worsens patient prognosis [ 283 ]. Another clinical trial revealed the immunosuppressive activity of 3-hydroxyanthranilic acid (3-HAA), which is a downstream metabolite in the kynurenine pathway [ 284 ].

High levels of AhR expression have been recognized as a signal for rapid disease progression. Hezaveh et al. [ 285 ] observed the activation of AhR in tumor-associated macrophages (TAMs) by microbiota-derived tryptophan metabolites in pancreatic ductal adenocarcinoma (PDAC). Moreover, deletion of AhR reduced tumor growth, increased the number of IFNg + CD8 + T-cells, and improved the efficacy of ICI treatment.

Indole-3-carboxaldehyde (3-IAld) exhibits great potential in modulating the immune response at the interface between microbes and the host immune system [ 286 ]. Researchers have found that 3-IAld in alters the composition of the gut microbiota and induces SCFAs production [ 287 ]. In addition, 3-IAld has been shown to alleviate irAEs by activating the AhR/IL-22 pathway, which targets the epithelial barrier to help maintain mucosal homeostasis [ 288 ].

According to Huang et al. [ 289 ], interventions such as prebiotics assist in the accumulation of the tryptophan catabolite valeric acid. Decreased Kyn/Trp ratios could suppress Treg cells and activate effector T cells, which will eventually enhance the efficacy of anti-PD-1 immunotherapy. In summary, these findings support the oncogenic effect of the kynurenine pathway and the antitumor effect of indoles.

Metabolites produced by the host and modified by the gut microbiota

Bile acids (BAs) are a group of metabolites synthesized from cholesterol and then formed by the gut microbiota [ 290 ]. Limited knowledge is available regarding the correlation between ICI treatment outcomes and BAs, while relatively more is known about the mechanism through which BAs modulate the host immune system.

A recent study revealed distinct BA features in Rs and NRs to ICI-treated HCC. Specifically, ursodeoxycholic acid (UDCA) was significantly more abundant in Rs, whereas lithocholic acid (LCA) was more abundant in NRs [ 291 ]. The antitumor effect of UDCA has been widely reported [ 292 ]. Various signaling pathways, immune cells, and cytokines, such as the epidermal growth factor receptor (EGFR)/ERK signaling pathway, NKT cells, and TGF-β, are involved in the protective effect of UDCA [ 293 , 294 , 295 ].

Secondary BAs such as deoxycholic acid (DCA) activate EGFR and protein kinase C, thus causing DNA damage and apoptosis and eventually leading to cancer cell proliferation [ 296 , 297 , 298 , 299 ].

Metabolites synthesized de novo by the gut microbiota

A recent study identified that A. muciniphila and B. pseudolongum utilize the inosine-adenosine 2A receptor(A2AR) signaling pathway to improve the efficacy of ICI therapy. The authors presumed that inosine activates T cells and reprograms the TME [ 300 ]. Based on their findings and other relevant studies, we identified several potential mechanisms through which inosine may influence immune responses to ICI therapy.

The immunomodulatory effects of inosine on immune cells could be a critical factor. Activation of the inosine-A2AR-cAMP-PKA signaling pathway leads to phosphorylation of the transcription factor cAMP response element–binding protein (CREB) [ 300 ]. Other research has shown that the microbiota–inosine–A2AR axis can influence the differentiation and expansion of Treg, CD8 + T, Th1, and Th2 cells and the production of cytokines [ 301 , 302 , 303 , 304 , 305 ].

Furthermore, inosine can support cell growth and T-cell functions as an alternative metabolic substrate. The high metabolic demands of cancer cells can limit the capacity of effector T cells by restricting available nutrients [ 306 , 307 , 308 ]. Wang et al. [ 309 ] demonstrated that inosine can relieve tumor-imposed metabolic restrictions on T cells. Specifically, effector T cells utilize the ribose subunit of inosine to activate central metabolic pathways and generate ATP and biosynthetic precursors.

Peptidoglycan

In a recent study, NOD2-active muropeptides generated by active enterococci with orthologs of the NlpC/p60 peptidoglycan hydrolase SagA were shown to improve the efficacy of ICI immunotherapy [ 310 ]. Further mechanistic exploration revealed that microbiota-derived peptidoglycans augment CD8 + T cells that express granzyme B and a particular monocyte population characterized by Cx3cr1 and Nr4a1 expression [ 39 ]. Accordingly, researchers suggested that specialized peptidoglycan remodeling activity and muropeptide-based strategies could be regarded as the future of next-generation immunotherapy.

Immune-related adverse events and the gut microbiota

A large spectrum of autoimmune responses is associated with ICIs due to their impact on immune cell activation [ 311 ]. Inflammatory side effects termed immune-related adverse events (irAEs) are frequently linked to the gastrointestinal tract, endocrine glands, skin, and liver during ICI treatment [ 312 , 313 , 314 , 315 , 316 ]. These potential irAEs reveal the necessity of multidisciplinary, collaborative management across the clinical spectrum [ 317 , 318 ]. In addition to identifying microbial signatures associated with the efficacy of ICI therapy, the microbiota composition and dysbiosis in the gut have also shown a connection with the incidence of irAEs (Additional file 1 : Table S2).

In terms of immunotherapy-related colitis, multiple studies have identified various microbial signatures and related signaling pathways that mediate the proinflammatory side effects of ICIs. Dubin et al. [ 319 ] reported a correlation between the abundance of specific bacterial taxa and subsequent colitis development. This report was followed by several studies that identified more irAE-colitis-associated gut microbes ranging from Firmicutes families to Streptococcus spp [ 196 , 200 , 209 , 236 , 238 ]. In addition to studies on colitis-induced bacteria, other studies have suggested that Bifidobacterium ameliorates colitis [ 320 ]. Researchers have demonstrated that Bifidobacterium breve and Lactobacillus rhamnosum can enhance the suppressive function of Treg cells by stimulating an IL-10/IL10Ra signaling loop [ 321 ].

These discoveries have provided opportunities to target gut microbes using strategies such as FMT or probiotics to decrease intestinal toxicity. Researchers in a case series utilizing FMT to abrogate ICI-associated colitis observed an increase in the proportion of Treg cells within the colonic mucosa [ 322 ]. Additionally, administration of the probiotic L. reuteri could ameliorate the immunopathology associated with ICIs by affecting the local number of ILC3s [ 323 ]. The microbial metabolite 3-IAld has demonstrated therapeutic potential in maintaining epithelial barrier function in the gut, which could help alleviate ICI-induced intestinal toxicity [ 286 ].

With the increased use of ICIs, irAEs are no longer limited to colitis but include all kinds of related diseases, such as diarrhea, pancreatitis, pruritus, and thyroid dysfunction. Researchers have identified various characteristics of the gut microbiome related to the increasing risk of irAEs [ 324 , 325 , 326 ]. Usyk et al. [ 327 ] applied this widely studied connection to predict the incidence of irAEs.

In summary, utilizing the microbiota composition as a prediction tool and therapeutic target for irAEs in ICI-treated patients may be a promising direction for treatment.

Gut microbiota modifications in response to cancer immunotherapy

Accumulating evidence has revealed how the gut microbiota and its metabolites interact with the host immune system to regulate antitumor immunity and immunotherapy responses. Therefore, modifications of the gut microbiota to enhance ICI treatment efficacy are promising approaches for therapeutic development. Here, we review preclinical and clinical trials that aimed to improve the clinical outcomes of patients treated with ICIs by altering gut microbes (Fig.  5 ). The main methods used for this purpose include FMT, dietary regulation, probiotics, prebiotics, and engineered microbial products.

Future intervention strategies to modificate gut microbiota in cancer immunotherapy. Targeting the association between the gut microbiome and cancer immunotherapy, modifying the gut microbiota with the latest intervention technologies could significantly advance the quality of individualized treatment. Listed here are the potential mechanisms behind the five microbiota modification strategies, which could be used to promote the efficacy of cancer immunotherapy in a precise manner. These intervention strategies are developed mainly based on current views of the crosstalk between the gut microbiota and the immune system. FMT, dietary regulation, probiotics, prebiotics, and engineered microbial products all can alter intestinal bacteria to enhance anti-tumor immune responses inside the TME, which consequently improve the efficacy of cancer immunotherapy

FMT is a well-established clinical approach for modulation of the gut microbiota [ 328 ]. Transplantation of the gut microbiota from a healthy donor restores intestinal microbial diversity in the recipient [ 329 ]. Currently, FMT is recommended by the FDA for treating recurrent Clostridium difficile infection [ 330 ].

Considering the unique microbial features of ICI responders, it is tempting to presume that FMT is applicable in immunotherapy. Several preliminary trials have explored coupling FMT with immunotherapy, and their results have indicated that FMT could induce the differential expression of T-cell and NK cell-related pathways in ways that control tumor growth and ameliorate the immune response [ 188 , 191 , 192 , 331 ].

Three recent studies have investigated the feasibility of introducing FMT through oral stool capsules in patients treated with ICIs. All of these studies revealed desirable outcomes, including an increased abundance of bacteria associated with response to anti-PD-1 therapy, activation of CD8 + T cells, and a decreased amount of IL-8-expressing myeloid cells. The microbiota sources were obtained from healthy stool donors [ 23 , 261 , 332 ]. These observations confirmed that FMT could alter the microbiota composition and reprogram immune and inflammatory factors to increase the efficacy of ICIs [ 333 ]. The safety data from Routy et al. [ 332 ] confirmed that FMT combined with anti-PD-1 therapy did not increase the incidence of irAEs. Additionally, Spreafico et al. utilized a microbial consortium, Microbial Ecosystem Therapeutic 4 (MET4), as an alternative to FMT in combination with ICIs in patients with advanced solid tumors. Their results suggested no worsening of ICI-associated irAEs when using MET4 [ 334 ]. Given these promising results, there are many ongoing clinical trials investigating the exact mechanism behind FMT-induced enhancement of ICI efficacy in larger patient cohorts (Additional file 1 : Table S3).

Recently, two live microbiome therapeutic products were approved by the FDA: RBX2660 and SER-109. Clinical trials on these products have shown that they reduce the incidence of recurrent Clostridioides difficile infection (rCDI) with a low risk of adverse events related to treatment. We summarized the detailed trial design and results of these products(Table  3 ).

Based on their innovativeness, RBX2660 and SER-109 were granted Breakthrough Therapy Status, Fast Track, and Orphan Drug designations by the FDA [ 335 , 336 ].

However, there is also considerable risk during FMT [ 337 ]. For example, a whole transplantation of the gut microbiota may sabotage the existing boundary of beneficial bacteria in the recipient, thereby causing infectious diseases [ 338 ]. Therefore, professional guidelines should be put in place to mandate presurgical safety screenings for donors, define standardized duration and delivery methods for the procedure, and build machine learning models that can to predict responses to minimize FMT-associated risks [ 339 , 340 , 341 ].

Dietary regulation

Recent studies have revealed the potential regulatory effect of diet on the gut microbiota [ 342 ]. Multiple studies have proven that dietary interventions can alter the composition of the gut microbiome. For instance, the standard Western diet (which is high in fat and carbohydrates and low in fiber) could induce gut dysbiosis, as it causes an increase in Firmicutes , Proteobacteria , Mollicutes , Bacteroides spp. , Alistipes spp. , Bilophila spp. , Enterobacteriaceae , Escherichia , Klebsiella , and Shigella while decreasing the abundance of beneficial bacteria Bacteroidetes , Prevotella , Lactobacillus spp. , Roseburia spp. , E. rectale , Bacillus bifidus and Enterococcus , leading to increased BA secretion and decreased downstream SCFA production [ 343 , 344 , 345 ]. Moreover, low-fat, high-fiber diets can improve the gut microbiome composition by shifting the microbiota composition toward and increase in the beneficial bacteria Prevotella and Bacteroides and a decreased in Firmicutes [ 346 ]. Therefore, dietary regulation via the gut microbiota could be a promising clinical strategy to improve the efficacy of cancer treatment [ 347 , 348 , 349 , 350 , 351 , 352 ].

One clinical study that focused on the impact of the food-gut axis on the response to ICIs revealed a positive correlation between high-fiber diets and improved responsiveness to anticancer immunotherapy. Specifically, higher expression of genes related to T-cell activation and the interferon response were observed in the high-fiber diet group, which were likely induced by fiber-fermenting bacteria through the production of SCFAs [ 353 ].

A ketogenic diet, which is a high-fat, low-protein, and low-carbohydrate diet, is well known for its ability to inhibit lactate-mediated tumoral immunosuppression and tumor cell metabolism [ 354 , 355 , 356 ]. Ferrere et al. studied the efficacy of combining a ketone-rich diet with immunotherapy [ 357 ] and reported that supplementation with ketone bodies could re-establish therapeutic responses when ICI treatment failed to reduce tumor growth on its own. A ketogenic diet could induce changes in the gut microbiota composition, leading to the expansion of CXCR3 + T cells and inhibition of the IFNγ-mediated upregulation of PD-L1 expression on myeloid cells.

Currently, many tentative clinical trials aimed at characterizing diet-induced alterations in the gut microbiota and their possible effects on immunotherapy efficacy are underway to better understand their relationship (Additional file 1 : Table S3).

Probiotics are defined as “live microorganisms which, when administered in adequate amounts, confer a health benefit to the host” [ 358 ]. Probiotics have been applied to prevent and treat multiple diseases [ 355 , 356 , 357 ] and specifically for cancer, Lactobacillus spp. and Bifidobacterium spp. strains were capable of relieving dysbiosis, enhancing anticancer immunity, and improving ICI treatment efficacy in recent studies [ 359 , 360 , 361 , 362 ].

The utilization of single probiotic strains has yielded exciting therapeutic effects when combined with cancer immunotherapy. Bifidobacterium supplementation has been shown to play a key role in improving ICI efficacy [ 22 , 363 ]. The probiotics Clostridium butyricum and Lactobacillus rhamnosus , and antibiotic-resistant lactic acid bacteria may also improve the therapeutic efficacy of ICIs as they increase the number of beneficial bacteria and reshape functional metagenomes [ 24 , 364 , 365 , 366 ]. In terms of A. muciniphila , researchers have identified an IL-12-dependent mechanism by which A. muciniphila triggers the recruitment of CCR9 + CXCR3 + CD4 + T lymphocytes into the TME to increase the efficacy of ICI treatments [ 188 ]. Increased T-cell function was also observed in CTLA-4 mAb-treated patients administered L.acidophilus . Zhuo et al. [ 367 ] reported that ICI efficacy could be enhanced by increasing the abundance of CD8 + T cells and effector memory T cells, as well as by decreasing the abundance of Treg cells and M2 macrophages in the TME.

Compared to single probiotic strains, a bacterial consortium may better represent the collective properties of the gut microbiota. Tanoue et al. [ 368 ] applied a bacterial consortium containing 11 commensal strains in tumor-bearing mice and identified a mechanism or enhancing ICI efficacy that was dependent on CD103 + DCs and major histocompatibility class Ia cells. A recent study validated the use of probiotics as a stand-alone therapy for treating tumors, where a mix of four Clostridiales species could exert antitumor effects by activating CD8 + T cells and increasing the immunogenicity of tumors [ 369 ].

Nevertheless, there is conflicting evidence on the benefits of probiotics marketed as dietary supplements [ 370 ]. Suez et al. [ 371 ] identified a delayed reconstitution of the gut mucosal microbiota using an 11-strain probiotic cocktail. Inconsistent clinical results also exist of the agonist effects of probiotic strains and formulations in immunotherapy have also been reported [ 353 ]. More efforts are needed to gain a thorough understanding of the effects of probiotics on immune responses and cancer immunotherapy (Additional file 1 : Table S3).

A prebiotic is defined as a substrate that is selectively utilized by host microorganisms to confer a health benefit [ 372 ]. Studies have shown that prebiotics can assist in promoting immunomodulatory effects, as well as stimulating the gut barrier and enhancing metabolic functions [ 373 ].

Prebiotics may improve the immunomodulatory effects of ICIs by altering the adundance of SCFAs. Researchers have shown that natural prebiotics, such as bilberry anthocyanin, pectin, the plant polysaccharide inulin, and ginseng polysaccharides, modulate anti-PD-1 therapy. These prebiotics can increase the amount of beneficial SCFAs, which further induces systemic memory T-cell responses and increases T-cell infiltration and activation in the TME [ 289 , 374 , 375 , 376 , 377 ]. Alternatively, artificial prebiotics such as AHCC® (a standardized extract of cultured Lentinula edodes mycelia) and castalagin also enhanced ICI efficacy by altering the gut microbiota composition and enhancing T-cell functions within the TME [ 378 , 379 ].

Engineered microbial products

With the development of genetic technology, engineered microbial products have attracted research interest worldwide. In contrast to the innate microbiota, these engineered microbes are designed to be sensitive to disease signals and respond to them at the site of onset [ 380 ]. They also contain bacteriophages, which modulate the composition of the gut microbiota.

To date, multiple reports have demonstrated the reliable delivery of antitumor benefits by engineered bacterial strains in many different contexts [ 381 , 382 , 383 , 384 , 385 ]. Here, we discuss how these microbes could be applied as a complement to anticancer immunotherapy. Binder et al. [ 386 ] demonstrated a powerful new therapeutic approach, that combines Salmonella typhimurium with PD-L1 blockade to activate the expansion of tumor-specific CD8 + T cells, resulting in the eradication of tumors. Similarly, Mkrtichyan et al. [ 387 ] observed an increase in CD8 + T-cell infiltration and antigen-specific immune responses in the periphery during anti-PD-1 immunotherapy after the administration of Listeria monocytogenes . These studies supported the hypothesis that microbes could indeed establish a more immunogenic microenvironment. Another approach to improve antitumor effects would be to enable metabolic modulation. Intertumoral injection of the Nissle 1917 E.coli strain increased the intracellular L-arginine concentration, triggered T-cell infiltration, and amplified the efficacy of PD-L1 blockade [ 388 ]. However, further technical refinements are still needed before the full-fledged clinical application of engineered bacteria can be achieved [ 389 ].

The utilization of bacteriophages as microbe-targeting vectors to induce immunomodulation has attracted extensive research interest [ 290 , 390 ]. Bacteriophages promote the eradication of cancer-promoting commensals while maintaining their influence on the surrounding microbiota. A bacteriophage-guided, biotic–abiotic hybrid nanosystem could also provide precise phage release within the TME to accurately remove only pro-tumoral bacteria. For instance, F. nucleatum -specific phages have been shown to augment the efficacy of ICIs as well as first-line chemotherapy treatments [ 391 , 392 ]. Notably, studies have revealed that correlations between specific bacteriophages and bacteria appear to be associated with FMT outcomes [ 393 , 394 ].

These engineered microbial products are promising for immunotherapy development, and more studies are needed to explore their potential application.

Challenges and future perspectives

In this review, we systematically examined current studies on the intricate relationship between the gut microbiota and the host immune system. Given the dynamic interactions among the gut microbiota, its metabolites, and various cancer immunotherapies including ICI, ACT, and CpG-ODN therapy, future studies should focus on discovering the underlying mechanisms of this modulatory effect, in addition to investigating distinct microbiota compositions. Recently, there has been accumulating evidence that the gut microbiota is a leading cause of irAEs in cancer immunotherapy. To minimize irAEs and improve immunotherapy safety, more studies are needed to develop novel interventions targeting commensal bacteria. Additionally, after reviewing the current therapeutic trials utilizing FMT, diet control, probiotics, prebiotics, and engineered microbial products combined with immunotherapy, we believe that there is still a tremendous need to explore the design of personalized methods of microbiota modification and strategies to optimize therapeutic efficacy.

Recent research on microbiota-cancer immunotherapy interactions shares the common concern of heterogeneity in trial design [ 5 ], which can be attributed to the lack of a uniform methodology during sample allocation, technology utilization, data quality control, and data analysis. To address this issue, a consortium-level effort is needed to construct a standardized protocol specifying certain requirements for microbial specimen type and origin, sample handling environment, and microbiota bioinformatics analysis [ 395 ]. In addition to the study design, dynamic alterations in the gut microbiota and time-dependent disease progression could also induce heterogeneity [ 396 , 397 ]. Therefore, consistent monitoring of the microbial composition throughout the disease course or exploration of the predictable patterns of microbial communities needs to be incorporated as a part of study protocols [ 398 ]. A recent study developed a computational method that exhibited promising potential for monitoring the dynamic alterations in gut microbes. This approach revealed the associations between drug exposure and the microbiome at high resolution, indicating the capacity to predict microbial changes and patient outcomes [ 399 ].

Moreover, the high degrees of biological inter- and intrapersonal variability of the gut microbiota imply that there is much more to learn in terms of individual heterogeneity [ 400 ]. Emerging spatial multiomics tools, especially single-cell techniques, are invaluable in deciphering the heterogeneous configurations of individuals at the bacterial strain level [ 401 , 402 ]. Despite the accumulating evidence of improved therapeutic outcomes in humans and preclinical model mice, there are still gaps in our knowledge regarding the modulating effects of the gut microbiota that hindering its clinical application. Most importantly, most studies have focused solely on observing the correlation between the gut microbiota and treatment outcomes rather than exploring the existence of any causality. Because the gut microbiota functions as a whole, the impact of modifying individual bacterial strains may have different effects on the collective properties of the entire gut microbiota beyond an individual strain. To advance the current research from association-based to mechanism-based, the application of synthetic biology in the human microbiota might be a critical tool [ 403 , 404 ].

In terms of gut microbiota modification, more functional studies and prospective clinical trials are needed to translate preclinical interventions targeting the gut microbiota into clinical applications in humans. One main challenge of applying experimental interventions in the clinic is that humans and animals do not share the same immune system. Another factor that cannot be ignored is differences in the gut microbiome composition and richness between rodents and humans. These limitations have restricted the translation of preclinical studies focusing on the gut microbiota. Therefore, the construction and characterization of the human gut microbiota in vitro could significantly improve the quality of individualized immunotherapy [ 405 ]. Furthermore, in situ genome engineering of the microbiota has also demonstrated promising potential for the regulation of existing microbial communities, which suggests its future utilization in the manipulation of cancer immunotherapy outcomes [ 406 ].

In summary, our knowledge about the intricate relationships among the gut microbiota, the host immune system, and cancer immunotherapy are still limited. By combining artificial intelligence applications with the emerging advances we mentioned above [ 407 ], future research should provide further insights into the crosstalk between the microbiota and clinical outcomes of immunotherapies, thus paving the way for the clinical application of gut microbiota interventions, as well as the development of personalized medicine for cancer management.

Availability of data and materials

Not applicable.

Abbreviations

Short-chain fatty acids

Trimethylamine N-oxide

Programmed cell death protein-1/programmed cell death protein-1-ligand 1

Fecal microbiota transplantation

Gastrointestinal

Cyclophosphamide

Helper T cell

Regulatory T cell

Antimicrobial peptides

Human defensing

Pattern recognition receptors

Pathogen-associated molecular patterns

Intestinal epithelial cells

Toll-like receptors

Nucleotide oligomerization domain

Nucleotide-binding domain and leucine-rich repeat-containing receptors

Phosphoinositide 3-kinase

Myeloid differentiation primary response gene 88

Zona occludens 1

Myosin light chain kinase

Polysaccharide A

Bacteroides fragili s

Interleukin-10

Extracellular-signal-regulated kinases

Mitogen-activated protein kinases

NOD-like receptor thermal protein domain associated protein

Proteus mirabilis

Akkermansia muciniphila

Tumor necrosis factor-α

Dendritic cells

Colony-stimulating factor 2

Innate lymphoid cells 3

Antigen-presenting cells

Plasmacytoid dendritic cells

Conventional dendritic cells

Interferon-I

Outer membrane vesicles

Natural killer cell

Segmented filamentous bacteria

B cell receptors

Histone deacetylase

Adenosine triphosphate

Regulatory B cells

Cytotoxic T lymphocytes

Tumor microenvironment

Cytotoxic T lymphocyte17

Transforming growth factor

Follicular helper T cell

Germinal center

T follicular regulatory cells

• Immune checkpoint inhibitors

Tumor-infiltrating lymphocytes

Breast cancer

Metastatic melanoma

Non-responders

Non-small-cell lung carcinoma

Hepatocellular carcinoma

Renal cell carcinoma

Colorectal carcinoma

Stimulator of interferon genes

Cytotoxic T lymphocyte-associated antigen-4

Inducible costimulatory

Progression-free survival

Overall survival

Adoptive cell transfer

Chimeric antigen receptor

Helicobacter hepaticus

Tertiary lymphoid structures

Unmethylated cytidine phosphate guanosine oligonucleotides

G protein-coupled receptors

Nuclear factor of activated T-cell

Aryl hydrocarbon receptor

3-Hydroxyanthranilic acid

Tumor-associated macrophage

Pancreatic ductal adenocarcinoma

Indole-3-carboxaldehyde

Ursodeoxycholic acid

Lithocholic acid

Epidermal growth factor receptors

Deoxycholic acid

Adenosine 2A receptor

CAMP response element–binding protein

• Immune-related adverse events

Recurrent clostridioides difficile infection

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Acknowledgements

This work was supported by the National Natural Science Foundation of China, No.82102998. The views expressed are those of the authors and not necessarily those of the NSF. We apologize for not being able to cite all the publications related to this topic due to space constraints of the journal.

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Zehua Li, Weixi Xiong, and Zhu Liang contributed equally to this work.

Authors and Affiliations

Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China

Zehua Li & Xuewen Xu

Chinese Academy of Medical Sciences (CAMS), CAMS Oxford Institute (COI), Nuffield Department of Medicine, University of Oxford, Oxford, England

Zehua Li & Zhu Liang

Department of Neurology, West China Hospital, Sichuan University, Chengdu, China

Weixi Xiong & Dong Zhou

Institute of Brain Science and Brain-Inspired Technology of West China Hospital, Sichuan University, Chengdu, China

Target Discovery Institute, Center for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, England

Departments of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China

Department of Neonatology, West China Second University Hospital of Sichuan University, Chengdu, China

Department of Functional Genomics, Medical University of Lodz, Lodz, Poland

Damian Kołat

Department of Biomedicine and Experimental Surgery, Medical University of Lodz, Lodz, Poland

Department of Urology, Churchill Hospital, Oxford University Hospitals NHS Foundation, Oxford, UK

Department of General Surgery and Gastric Cancer Center, West China Hospital, Sichuan University, Chengdu, China

Linyong Zhao

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ZL, WX and ZL contributed equally to this work; the conception and design of the study: LZ, ZL; acquisition of data from published papers: ZL, JW, ZZ, DK, XL, XX, DZ; analysis and interpretation of data: ZL, WX, LZ; manuscript preparation and manuscript editing: ZL, WX, ZL; manuscript review and corresponding author: L.Z.

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Li, Z., Xiong, W., Liang, Z. et al. Critical role of the gut microbiota in immune responses and cancer immunotherapy. J Hematol Oncol 17 , 33 (2024). https://doi.org/10.1186/s13045-024-01541-w

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Received : 25 October 2023

Accepted : 03 April 2024

Published : 14 May 2024

DOI : https://doi.org/10.1186/s13045-024-01541-w

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